CN106849189B - Consider the micro-capacitance sensor economy and method for optimizing stability of renewable energy randomness - Google Patents
Consider the micro-capacitance sensor economy and method for optimizing stability of renewable energy randomness Download PDFInfo
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Abstract
The present invention provides a kind of micro-capacitance sensor economy and method for optimizing stability for considering renewable energy randomness, includes the following steps: step 1, predicts renewable energy power output；Step 2 establishes micro-capacitance sensor economical optimum model；Step 3 establishes micro-capacitance sensor estimation of stability index system；Step 4 determines micro-capacitance sensor active power controller strategy；Step 5 coordinates and optimizes micro-capacitance sensor economy and stability.The present invention is based on droop control methods, improve isolated research of the tradition for micro-capacitance sensor economy and stability, effectively the two is combined, it can adapt to a variety of different changes of operating modes, by differential evolution algorithm to sagging coefficient Optimization Solution, there is good dynamic stability, established model method is rationally, effectively while can be realized micro-capacitance sensor good economic.
Description
Technical field
Coordinate and optimize field the present invention relates to micro-capacitance sensor economy and stability, specifically a kind of consideration renewable energy with
The micro-capacitance sensor economy and method for optimizing stability of machine.
Background technique
As distributed generation resource permeability gradually increases, the renewable energy power generations such as wind-power electricity generation and photovoltaic power generation are had
Randomness and non-confirmability, not only bring challenge to the economic load dispatching of micro-capacitance sensor, at the same increase micro-capacitance sensor stablize fortune
Capable risk.Micro-capacitance sensor can stable operation not only to solve how to keep the stability problem under isolated operation, while also wanting
Guarantee to turn isolated network grid-connected, the stable operation in the various dynamic processes such as acute variation occurs for microgrid operation mode.Therefore, how
Under the premise of renewable energy power output is uncertain, considers the violent variation of the method for operation in microgrid operational process, realize to micro-
The economic optimization of power grid and stable coordination are the difficult points nowadays studied.
Currently, domestic and foreign scholars have done numerous studies for the operation of micro-capacitance sensor economic optimization, and model is established, these moulds
Type is too simple, consideration be micro-capacitance sensor whole year economic load dispatching, model be not able to satisfy micro-capacitance sensor real-time dynamic scheduling operation, and
And the micro-capacitance sensor dynamic stability when distributed generation resource is contributed and changed is not considered, in fact, microgrid economic optimum operating scheme is simultaneously
It cannot be guaranteed that system has preferable stability.And for only having inquired into micro-capacitance sensor isolated operation mould in the research of micro-capacitance sensor stability
Stability problem under formula is verified the validity of institute's climbing form type and strategy by microgrid frequency and power response, lacked to micro-
Economic power system comprehensively considers.
Summary of the invention
For the present invention aiming at the problem that micro-capacitance sensor economic optimization and stability study cannot isolate progress, providing a kind of consideration can
The micro-capacitance sensor economy and method for optimizing stability of renewable sources of energy randomness pass through when not increasing add-on device cost
Microgrid economic stability optimized operation is realized in sagging control.
The present invention adopts the following technical scheme that realize:
A kind of micro-capacitance sensor economy and method for optimizing stability considering renewable energy randomness, includes the following steps:
Step 1 predicts renewable energy power output: using Latin Hypercube Sampling method (LHS) by renewable energy
Output power is discrete to turn to multiple scenes, and obtains ten calculating scenes using scene cutting method；
Step 2 establishes micro-capacitance sensor economical optimum model: micro-capacitance sensor economic load dispatching, which refers to, is meeting power-balance constraint, peace
Realize that micro-capacitance sensor operating cost is minimum under the conditions of row constraint, unit output constraint and corresponding context restrictions for the national games etc..Micro-capacitance sensor
Operating cost mainly includes conventional power generation unit fuel cost, operation and maintenance and polluted gas discharge costs.
Micro-capacitance sensor economic optimization target are as follows:
In formula: Nt,Ni, and NkNumber of segment, adjustable micro- source number, blowdown classification number, P when respectivelyt i,newIt is i-th of controllable micro- source
In the output power of t moment, FCi t(Pt i,new) it is its fuel cost, αkIt is the processing cost of kth kind waste, EFi,kIt is i-th
The waste discharge factor in a controllable micro- source.For wind-driven generator photovoltaic power generation unit, polluted gas discharge is zero.
Wherein the fuel cost in controllable micro- source is FCi t(Pt i,new), i-th of controllable micro- source DDER (dispatchable
Distributed energy resource) it can be indicated by the quadratic function of its output power in the fuel cost of t moment.
In formula: ai、bi、ciFor the fuel cost coefficient of controllable unit, can be obtained by its consumption characteristic curve fitting.
Other than conventional constraint condition, the context restrictions condition of system are as follows:
Wherein, superscript s indicates different scenes.1st equation is the power-balance constraint under different scenes；2nd
Equation is the generator output constraint under different scenes；3rd, 4 equation is the energy-storage system constraint under different scenes；5th, 6
A equation is that the system under different scenes runs Reserve Constraint；7th, 8 equation is system frequency and voltage under different scenes
Constraint；
Step 3 establishes micro-capacitance sensor estimation of stability index system: in view of renewable energy output power randomness with
Load time variation, operating condition of the micro-capacitance sensor in isolated power grid change constantly, thus main to microgrid using ITAE index
State variable is evaluated, such as the output power of system frequency, busbar voltage and each micro- source.ITAE is that comprehensive consideration is measured
The index of error and regulating time can instruct to be measured in disturbance transitional processes, and regulating time is short, and overshoot is small, oscillation
Decaying rapidly, has good practicability.
Using ITAE as the objective function of estimation of stability index are as follows:
Wherein: k is discrete time, k0It is wind-force and load variations initial time；T0It is that micro-capacitance sensor reaches new steady state time；W
It is Eabs(k) weighting matrix；Eabs(k) it indicates each absolute error phasor being measured at the k moment, can be acquired by following formula:
Wherein:It is the new steady frequency of t moment micro-capacitance sensor,It is the new steady state voltage of t moment micro-capacitance sensor bus j,
NjIt is bus nodes number,It is the reactive power of i-th of controllable micro- source t moment output, ft (k),It is t respectively
The system frequency of moment measurement, busbar voltage, controllable micro- source active power of output and reactive power；
Step 4 determines micro-capacitance sensor active power controller strategy: in more DG (Distributed generations) parallel connection
When operation, the distribution of traditional sagging coefficient for each controllable micro- source is to distribute to realize based on each self-capacity, mp1P1=
mp2P2=...=mpnPn, there is no consider economy and stability principle.Micro-capacitance sensor proposed by the present invention based on sagging coefficient
Economic Scheduling Policy is the supply-demand mode by balancing active power to the judgement of micro-capacitance sensor frequency.Controllable micro- source i output
Active power is by mpiIt determines.Under scene s scene, controllable micro- source i is in t moment output power Pi t,sWith system frequency ωt,sRelationship
It can be derived by following formula:
In formula: mpi t-1It is controllably micro- source i in the sagging coefficient at t-1 moment.
Step 5 coordinates and optimizes micro-capacitance sensor economy and stability:
(1) comprehensive coordination Optimized model is determined.
There is many uncertain factors in micro-capacitance sensor, such as the uncertainty that blower and photovoltaic are contributed, load at runtime
Predict error, the variation of energy storage device operating condition etc..These factors are not only the key of microgrid economic load dispatching, and system is maintained to stablize
Important means.To reduce operating cost, the sagging coefficient in each schedulable micro- source should according to the size of micro- source operating cost into
Row distribution, meanwhile, the stability of micro-capacitance sensor is also largely influenced by sagging coefficient.Point of inappropriate sagging coefficient
Match, when microgrid power output or load change, in fact it could happen that biggish hyperharmonic oscillatory process.In order to realize the economy of microgrid
Property and preferable stability, the invention proposes the multiple targets optimized comprising operating cost and ITAE criterion to sagging coefficient
Model:
Min C=ω1×f1+ω2×SC×f2
Wherein: f1For optimal cost, f2For ITAE evaluation index, SC is zoom factor, ω1And ω2Corresponding weighting because
Son.
The value of different specific item scalar functions usually changes within the scope of certain variable-length, some specific item scalar functions may always by
Other sub-goals are dominated.In order to keep evaluation more effective, normalizing is carried out to each specific item scalar functions using fuzzy logic theory
Change processing, to two sub- objective function f1、f2Normalization.
Then synthesizing and coordinating Optimized model is
Fi=ω1·μ(f1,i)+ω2·μ(f2,i)
Solution to above-mentioned model is to each controllable micro- sagging Coefficient m in sourcepiOptimization.
(2) comprehensive coordination Optimized model is optimized
Above-mentioned proposed optimization problem is a constrained nonlinear systems problem, the differential evolution algorithm used herein
(DE) have without the property led, can effectively solve the characteristic of optimal result, after calculating target function, can check all constraints
Condition will increase a penalty term if violating any constraint.
The economic stability optimization process of entire micro-capacitance sensor mainly has 2 final steps: being to economy and stabilization first
The weighted factor of property index is determined, and is then optimized by differential evolution algorithm to objective function.
Further, the step 1 specifically: first by sampling different periods blower and photovoltaic power generation output power,
The scene set with probability distribution is generated, then chooses representative expectation scene by way of abatement polymerization.
Further, specific steps are optimized to comprehensive coordination Optimized model in step 5 are as follows:
A, weighted factor is determined
By ω1And ω2Two sections are divided into 10 subintervals respectively with 0.1 step-length, to obtain two groups of specific item offers of tender
Number f1,IAnd f2,I；
B, Optimization Solution
Under different weight distribution scenes, comprehensive coordination Optimized model is optimized, is obtained a series of excellent
Change as a result, as C in following formulaiWhen for minimum, as optimal solution:
The present invention has the advantage that
1, it is based on droop control method, improves isolated research of the tradition for micro-capacitance sensor economy and stability, effectively
The two is combined, provides a kind of new thinking for the economic load dispatching of micro-capacitance sensor；
2, for micro-capacitance sensor in each micro- source power output and load variations, ITAE index proposed by the present invention being capable of Efficient Characterization microgrid
The stability of system, and each controllable micro- sagging coefficient in source has large effect to ITAE index, it can be real by sagging control
Now to the adjusting of micro-capacitance sensor stability；
3, the optimisation strategy that microgrid economy proposed by the present invention is mutually coordinated with stability can adapt to a variety of different fortune
Line mode variation, by differential evolution algorithm to sagging coefficient Optimization Solution, while can be realized micro-capacitance sensor good economic
With good dynamic stability, established model method is rationally, effectively.
Detailed description of the invention
Fig. 1 is micro-capacitance sensor structural schematic diagram；
Fig. 2 is blower output power scene；
Fig. 3 is photovoltaic output power scene；
Fig. 4 is the droop characteristic figure in more controllable micro- sources；
Fig. 5 is majorized function convergence curve；
Fig. 6 is system frequency response correlation curve, and wherein Fig. 6 (a) is the lower system frequency response curve of mode 1, Fig. 6 (b)
For the lower system frequency response curve of mode 2；
Fig. 7 is system voltage amplitude response correlation curve；Wherein Fig. 7 (a) is the lower voltage magnitude response curve of mode 1, Fig. 7
It (b) is voltage magnitude response curve under mode 2；
Fig. 8 is DDER2 active power curves, and wherein Fig. 8 (a) is the lower DDER2 active power curves of mode 1, wherein Fig. 8
It (b) is DDER2 active power curves under mode 2；
Fig. 9 is DDER1 reactive capability curve, and wherein Fig. 9 (a) is the lower DDER1 reactive capability curve of mode 1, wherein Fig. 9
It (b) is DDER1 reactive capability curve under mode 2.
Specific embodiment
Below in conjunction with the attached drawing in the present invention, the technical solution in the present invention is clearly and completely described.
A kind of micro-capacitance sensor economy and method for optimizing stability considering renewable energy randomness, includes the following steps:
Step 1 predicts renewable energy power output.
Micro-capacitance sensor structural representation map analysis (as shown in Figure 1) is combined to contain in the isolated operation mode of micro-capacitance sensor first
Three controlled distributions decline source (DDER), wind-driven generator (WTG), photo-voltaic power generation station (PV), energy-storage system (ESS), Yi Jimu
Load on line.In view of actual conditions, load is divided into interruptible load (IL) and uninterrupted load (NL) by the present invention.
To wind-driven generator, photo-voltaic power generation station carries out power output prediction, will be renewable using Latin Hypercube Sampling method (LHS)
Energy output power is discrete to turn to multiple scenes.First by sampling different periods blower and photovoltaic power generation output power, generate
Then scene set with probability distribution chooses 10 representative expectation scenes by way of abatement polymerization.Wind
Machine and photovoltaic scene of specifically contributing are shown in Fig. 2 and Fig. 3.
Step 2 establishes micro-capacitance sensor economical optimum model.Micro-capacitance sensor economic load dispatching, which refers to, is meeting power-balance constraint, peace
Realize that micro-capacitance sensor operating cost is minimum under the conditions of row for the national games constraint, unit output constraint etc..Microgrid operating cost mainly includes normal
Advise generating set fuel cost, operation and maintenance and polluted gas discharge costs.
Microgrid economic optimization target are as follows:
Wherein the fuel cost in controllable micro- source is FCi t(Pt i,new), fuel cost of i-th of controllable micro- source DDER in t moment
It can be indicated by the quadratic function of its output power.
In formula: ai、bi、ciFor the fuel cost coefficient of controllable unit, can be obtained by its consumption characteristic curve fitting.
The fuel cost in three controllable micro- sources can be shown in Table with the output power limit, blowdown cost coefficient and capacity of energy storing device
1, table 2 and table 3.
Controllable micro- source (DDERs) fuel cost of table 1
Controllable micro- source (DDERs) the blowdown cost of table 2
3 energy-storage system of table (ESS) parameter
The total load of micro-capacitance sensor day part is as shown in table 4, wherein 50% non-interruptible load.
4 microgrid day part load of table
Step 3 establishes micro-capacitance sensor estimation of stability index system.In view of renewable energy output power randomness with
Load time variation, operating condition of the micro-capacitance sensor in isolated power grid change constantly, thus main to microgrid using ITAE index
State variable is evaluated, such as the output power of system frequency, busbar voltage and each micro- source.ITAE is that comprehensive consideration is measured
The index of error and regulating time can instruct to be measured in disturbance transitional processes, and regulating time is short, and overshoot is small, oscillation
Decaying rapidly, has good practicability.
Using ITAE as the objective function of estimation of stability index are as follows:
Wherein: k is discrete time, k0It is wind-force and load variations initial time；T0It is that system reaches new steady state time；W is
Eabs(k) weighting matrix；Eabs(k) it indicates each absolute error phasor being measured at the k moment, can be acquired by following formula:
Step 4 determines micro-capacitance sensor active power controller strategy.Microgrid economy tune proposed by the present invention based on sagging coefficient
Degree strategy is the supply-demand mode by balancing active power to the judgement of microgrid frequency.The active power of controllable micro- source i output
By mpiIt determines.Under scene s scene, micro- source i is in t moment output power Pi t,sWith system frequency ωt,sRelationship can be pushed away by following formula
It leads:
In formula: mpi t-1For micro- source i the t-1 moment sagging coefficient.
Controllably the relationship of the sagging coefficient of micro- source power and system frequency is as shown in Figure 4.
Step 5 coordinates and optimizes micro-capacitance sensor economy and stability.
(1) comprehensive coordination Optimized model is determined.
To reduce operating cost, the sagging coefficient in each schedulable micro- source should be divided according to the size of micro- source operating cost
Match, meanwhile, the stability of micro-capacitance sensor is also largely influenced by sagging coefficient.The distribution of inappropriate sagging coefficient,
When microgrid power output or load change, in fact it could happen that biggish hyperharmonic oscillatory process.In order to realize microgrid economy and
Preferable stability, the invention proposes the multiple target moulds optimized comprising operating cost and ITAE criterion to sagging coefficient
Type.
Min C=ω1×f1+ω2×SC×f2
Wherein: f1For optimal cost, f2For ITAE evaluation index, SC is zoom factor, ω1And ω2Corresponding weighting because
Son.
The value of different specific item scalar functions usually changes within the scope of certain variable-length, some specific item scalar functions may always by
Other sub-goals are dominated.In order to keep evaluation more effective, normalizing is carried out to each specific item scalar functions using fuzzy logic theory
Change processing, to two sub- objective function f1、f2Normalization.
Then Integrated Optimization Model is
Fi=ω1·μ(f1,i)+ω2·μ(f2,i)
Solution to above-mentioned model is to each controllable micro- sagging Coefficient m in sourcepiOptimization.
(2) collective model is optimized
The economic stability optimization process of entire micro-capacitance sensor mainly has 2 final steps: being to economy and stabilization first
The weighted factor of property index is determined, and is then optimized by differential evolution algorithm to objective function, specific steps
It is as follows:
A, weighted factor is determined
By ω1And ω2Two sections are divided into 10 subintervals respectively with 0.1 step-length, to obtain two groups of specific item offers of tender
Number f1,IAnd f2,I。
5 weighting scheme of table
B, Optimization Solution
Under different weight distribution scenes, complex optimum function is optimized, obtains a series of optimization knot
Fruit, as C in following formulaiWhen for minimum, as optimal solution.
Above-mentioned proposed optimization problem is a constrained nonlinear systems problem, the differential evolution algorithm used herein
(DE) have without the property led, can effectively solve the characteristic of optimal result, after calculating target function, can check all constraints
Condition will increase a penalty term if violating any constraint.Fig. 5 is Optimization Solution convergence curve.
The optimal sagging optimization calculated result of micro-capacitance sensor is as shown in table 6.
The optimal sagging coefficient of 6 microgrid of table
Table 7 illustrates microgrid economy and stability under Different Optimization strategy.Under the control of optimal sagging coefficient, it is
The f of system1Operating cost is 1483.3 yuan, ITAE criterion f2It is 0.0229, at this point, contributing scene about in different wind-powered electricity generation and photovoltaic
Under beam, microgrid economy is coordinated optimal with stability.
7 microgrid economy of table and stable coordination optimization result
In order to prove the microgrid economic dispatch program of the invention mentioned by the transient state and dynamic stability of the system that improves, comparison
Analyze the dynamic stability to micro-capacitance sensor in the case of economy and stability different weights.
Fig. 6-Fig. 9 is respectively the frequency response of system under two kinds of different scheduling strategies, is responded to bus voltage amplitude, controllably
It the active power of micro- source (DDER) 2 and is responded with controllable micro- 1 reactive power of source (DDER).Mode 1: optimal scheduling strategy (ω1=
0.5,ω2=0.5), mode 2: the Optimum Economic scheduling (ω of stability is not considered1=1, ω2=0).
As can be seen that using optimal scheduling strategy (ω from Fig. 6-Fig. 91=0.5, ω2=0.5) when, microgrid has preferable
Dynamic stability, when blower contribute and load variation when, the overshoot that system transients process has is small, and regulating time is few, and
Can vibrate being capable of rapid decay.All monitoring quantities such as voltage, frequency, the power output in adjustable micro- source allow model without departing from system
It encloses.
If only considering that operating cost is minimum, i.e. ω 1=1, ω 2=0, when service condition changes, system is moved
Step response is poor, and system will lose stabilization.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Belong to those skilled in the art in the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of, all answers
It is included within the scope of the present invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (3)
1. a kind of micro-capacitance sensor economy and method for optimizing stability for considering renewable energy randomness, it is characterised in that including such as
Lower step:
Step 1, to renewable energy power output predict: using Latin Hypercube Sampling method by renewable energy output power from
Dispersion is multiple scenes, and obtains ten calculating scenes using scene cutting method；
Step 2 establishes micro-capacitance sensor economical optimum model: micro-capacitance sensor economic optimization target is
In formula: Nt,Ni, and NkNumber of segment, controllable micro- source number, blowdown classification number, P when respectivelyt i,newIt is i-th of controllable micro- source in t
The output power at moment,It is its fuel cost, αkIt is the processing cost of kth kind waste, EFi,kBe i-th can
Control the waste discharge factor in micro- source；I-th of controllable micro- source DDER can be by the secondary letter of its output power in the fuel cost of t moment
Number indicates:
In formula: ai、bi、ciFor the fuel cost coefficient of controllable unit；
Step 3 establishes micro-capacitance sensor estimation of stability index system: using ITAE as the objective function of estimation of stability index are as follows:
Wherein: k is discrete time, k0It is wind-force and load variations initial time；T0It is that micro-capacitance sensor reaches new steady state time；W is
Eabs(k) weighting matrix；Eabs(k) it indicates each absolute error phasor being measured at the k moment, can be acquired by following formula:
Wherein:It is the new steady frequency of t moment micro-capacitance sensor,It is the new steady state voltage of t moment micro-capacitance sensor bus j, NjIt is
Bus nodes number,It is the reactive power of i-th of controllable micro- source t moment output, ft(k),When being t respectively
Carve the system frequency of measurement, busbar voltage, controllable micro- source active power of output and reactive power；
Step 4 determines micro-capacitance sensor active power controller strategy: the active power of controllable micro- source i output is by mpiIt determines, in scene s
Under scene, controllable micro- source i is in t moment output power Pi t,sWith system frequency ωt,sRelationship is derived by following formula:
In formula: mpi t-1It is controllably micro- source i in the sagging coefficient at t-1 moment；
Step 5 coordinates and optimizes micro-capacitance sensor economy and stability
(1) comprehensive coordination Optimized model is determined
Min C=ω1×f1+ω2×SC×f2
Wherein: f1For the micro-capacitance sensor economic optimization target that step 2 determines, f2For the ITAE evaluation index that step 3 determines, SC is contracting
Put the factor, ω1And ω2It is corresponding weighted factor, place is normalized to each specific item scalar functions using fuzzy logic theory
Reason, to two sub- objective function f1、f2Normalization, then synthesizing and coordinating Optimized model is
Fi=ω1·μ(f1,i)+ω2·μ(f2,i)
The constraint condition for synthesizing and coordinating Optimized model is conventional power-balance, generator output, energy storage, runs spare, system
Frequency and voltage and corresponding context restrictions；
(2) comprehensive coordination Optimized model is optimized: first to the weighted factor ω of economy and stability indicator1With
ω2It is determined, then comprehensive coordination Optimized model is optimized by differential evolution algorithm.
2. the micro-capacitance sensor economy and method for optimizing stability of renewable energy randomness are considered as described in claim 1,
It is characterized in that: the step 1 specifically: have first by sampling different periods blower and photovoltaic power generation output power, generation
Then the scene set of probability distribution chooses representative expectation scene by way of abatement polymerization.
3. the micro-capacitance sensor economy and method for optimizing stability of renewable energy randomness are considered as described in claim 1,
It is characterized in that: specific steps being optimized to comprehensive coordination Optimized model in step 5 are as follows:
A, weighted factor is determined
By ω1And ω2Two sections are divided into 10 subintervals respectively with 0.1 step-length, to obtain two groups of specific item scalar functions
f1,IAnd f2,I；
B, Optimization Solution
Under different weight distribution scenes, comprehensive coordination Optimized model is optimized, obtains a series of optimization knot
Fruit, as C in following formulaiWhen for minimum, as optimal solution:
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