CN107017656A - A kind of active distribution system Optimization Scheduling for considering quality of power supply lifting - Google Patents
A kind of active distribution system Optimization Scheduling for considering quality of power supply lifting Download PDFInfo
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- H02J3/383—
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H02J3/386—
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
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- 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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Abstract
The invention discloses a kind of active distribution system Optimization Scheduling for considering quality of power supply lifting, it is specially：After the control variable of schedulable resource in acquisition active distribution system, the mathematical modeling of active distribution system Optimized Operation is set up, then, distribution network parameters are initialized, the new solution vector for meeting constraints, i.e., new harmony are produced by remembering library searching, 3 kinds of modes of local dip and random selection；Reject the worst solution in harmony storehouse, new harmony vector is put into harmony data base simultaneously, new harmony vector sum step 5 is generated in repeat step 4, produce new harmony to update harmony data base by successive ignition, when renewal reaches maximum iteration, export the minimum harmony of optimal solution, i.e. target function value, i.e. optimal scheduling scheme.The method of the present invention overcomes the shortcoming that existing single object optimization method does not consider distributed power source voltage regulation capability, and principle is simple, be easily achieved.
Description
Technical field
The invention belongs to distributed power generation access field of power distribution, it is related to a kind of active for considering quality of power supply lifting and matches somebody with somebody
Electric system Optimization Scheduling.
Background technology
The problems such as energy shortage, environmental pollution, drives clean energy resource generation technology fast development, following power distribution network ecology potential
Necessarily make full use of clean reproducible energy to generate electricity, meet all-embracing, the realization generated electricity to a large amount of clean reproducible energies
The hypersynchronous of renewable energy power generation and fully dissolve.But the permeability generated electricity with distribution type renewable energy is in electric power
Continuous improvement in each level of system, the method for operation of power distribution network compared with not accessing the power distribution network of distributed power source originally with becoming
More complicated, the hypersynchronous that distribution type renewable energy generates electricity also generate very big to the supervision method and economy of power distribution network
Influence, conventional electrical distribution net, which exists, dissolves that clean reproducible energy scarce capacity, self-regulation ability be low, scheduling mode falls behind etc.
Shortcoming, therefore, the concept of active distribution system is arisen at the historic moment.
The notable feature of active distribution system shows distributed generation unit, energy-storage units in access power distribution network etc. all
It is controllable, distributed power source in active distribution system will participate in the traffic control of power distribution network, and not conventional electrical distribution system
In simple connection.This causes active distribution system Optimization Scheduling to be active management distributed power source, realizes network security,
The core technology of economical operation.However, due to the fluctuation of intermittent renewable energy generating power output, energy storage device itself
The correlation of charging and discharging state in dispatching cycle caused by energy limitation so that the Optimization Scheduling of active distribution system is very
Complexity, therefore, for the feature of active distribution system, studies its Optimization Scheduling significant.
At present, domestic and foreign scholars propose many methods on the Optimized Operation of distribution system containing distributed power source, for example
1) in the active distribution system dispatching cycle based on intelligent algorithm economical operation Optimization Scheduling；2) distributed power source is considered
The micro-capacitance sensor multiple target electric energy optimizing scheduling problem of constraint.But, the 1) class method be to be carried out on the basis of single object optimization
, in terms of the safe operation for not accounting for active distribution system；The 2) class method be to be adjusted for the multiple-objection optimization of micro-capacitance sensor
Degree method, because active distribution system and micro-capacitance sensor have certain difference, for micro-capacitance sensor Multiobjective Optimal Operation method not
Active distribution system can be directly used in.
The content of the invention
It is an object of the invention to provide a kind of active distribution system Optimization Scheduling for considering quality of power supply lifting, overcome
Existing single object optimization method does not consider the shortcoming of distributed power source voltage regulation capability, and principle is simple, be easily achieved.
The technical solution adopted in the present invention is, a kind of to consider the active distribution system Optimized Operation side that the quality of power supply is lifted
Method, specifically implements according to following steps：
Step 1, the control variable of the schedulable resource in active distribution system is obtained；
Step 2, the mathematical modeling of active distribution system Optimized Operation is set up；
Step 3, initialization distribution network parameters, initialization algorithm iterations, initial harmony number of vectors scheduling algorithm parameter,
Generate initial harmony storehouse；
Step 4, the new constraints that meets is produced by remembering library searching, 3 kinds of modes of local dip and random selection
Solution vector, i.e., new harmony；
Step 5, reject the worst solution (i.e. the maximum harmony of target function value is vectorial) in harmony storehouse, at the same by new harmony to
Amount is put into harmony data base,
Step 6, new harmony vector sum step 5 is generated in repeat step 4, produces new harmony to update by successive ignition
Harmony data base, when renewal reaches maximum iteration, exports the minimum harmony of optimal solution, i.e. target function value, i.e., optimal
Scheduling scheme.
The features of the present invention is also resided in,
In described step 1, the dominant vector of described schedulable scheduling of resource operation is expressed as [QDG1,…,QDGh,
PDG1,…,PDGn,PE1,…,PEm]T；
Wherein, preceding h component (QDG1,…,QDGh) represent the idle power output of intermittent distributed power source, middle n
Component (PDG1,…,PDGn) represent gas turbine active power output, last m component (PE1,…,PEm) represent energy storage device
Charge-discharge electric power；
Described step 2 specifically,
Step 2.1, shown in the quality of voltage lifting object function such as formula (7) of foundation, economical operation object function such as formula (8)
It is shown,
In quality of voltage lifting object function (7), Δ U is voltage deviation amount, and K was represented in dispatching cycle
Comprising scheduling phase number；N represents node total number；ΔUiFor the voltage deviation amount of the i-th node；δU
For node voltage maximum allowable offset amount, the calculation formula of function phi is：
In economical operation object function (8), C is operating cost, and Δ T represents the duration of unit scheduling phase；L represents main
The feeder line number that dynamic distribution system is included；K represents the scheduling phase number included in dispatching cycle；N represents to connect in active distribution system
The gas turbine sum entered；ClAnd P (t)l(t) represent that feeder line l receives the electricity price cost of bulk power grid electric energy and big in t respectively
The power that power network is conveyed to feeder line l；CjAnd P (t)DG- j (t) represent respectively j-th of gas turbine t cost of electricity-generating and
Active output；
Step 2.2, using fuzzy set theory method, by the fuzzy membership function formula of linear segmented form, by multiple target
It is converted into the single-object problem of total satisfaction index.Fuzzy membership function formula and final single object optimization functional expression
It is as follows respectively：
Max λ=min (μ1,μ2) (11)
In fuzzy membership function formula (10), μiFor the degree of membership of sub-goal, wherein, μ1Represent quality of voltage lifting mesh
Target degree of membership, μ2Represent the degree of membership of economical operation target；For the higher limit of specific item scalar functions, i.e. voltage deviation amount and fortune
The higher limit of row expense；fiFor the theoretical optimal objective value of sub-goal, the i.e. theoretially optimum value of voltage deviation amount and operating cost.It is single
In objective optimization functional expression (11), λ represents the degree of membership of total satisfaction, i.e. quality of voltage lifting target and economical operation target
Minimum value；
Active distribution system Optimized Operation mathematical modeling also includes following constraints：
Umin≤Ui≤Umax (14)
Formula (12) represents active distribution system trend constraint；Formula (13) represents active distribution system power-balance constraint；Formula
(14) represent that active distribution system node voltage is not out-of-limit.In formula (12), Pa、QaFor each node active and reactive power, Va、VbPoint
Not Wei node a, b voltage magnitude, Gab、BabFor the conductance between the real and imaginary parts of bus admittance matrix element, i.e. node ab
And susceptance, θabFor the phase angle difference at circuit ab two ends；In formula (13), PDG_iThe active power output of i-th of distributed power source is represented,
PlinkRepresent that active distribution system interacts power with the electric energy of major network；PLRepresent active power distribution system load；PlossRepresent actively to match somebody with somebody
Electric system network loss；In formula (14), Umax、UminMaximum, the minimum value of node voltage, U are represented respectivelyiRepresent the voltage of node i.
Described step 3 is specifically implemented according to following steps：
Step 3.1, harmony data base HM initialization rule is：
In formula,For control variable, idle power output, combustion gas corresponding to the intermittent distributed power source in step 1
The charge-discharge electric power of the active power output of turbine and energy storage device；xi.max, xi.minBe respectively i-th dimension variable-value maximum,
Minimum value；Random (0,1) is the random number between 0~1, j ∈ [1, HMS], HMS represent in harmony data base HM it is initial and
The number of sound vector.
Step 3.2, the harmony data base HM of formation is designated as：
In formula,For j-th of scheduling strategy of i-th of distributed power source.H subscale before the every a line of harmony data base HM
Show the idle power output Q of intermittent distributed power sourceDG1,…,QDGh, the h+1~h+n representation in components gas turbine have
Work(power output PDG1,…,PDGn, the charge-discharge electric power P of the h+n+1~h+n+m representation in components energy storage deviceE1,…,PEm；
Step 3.3, control strategy of the blower fan and photovoltaic cells of access active distribution system in dispatching cycle is：In formula, QiRepresent that i-th of the idle of scheduling phase is exerted oneself,
The active output of gas turbine is accessed into the gas turbine of active distribution system in dispatching cycle as control variable
Control strategy is：
Represented using the dump energy of energy storage device dispatching cycle control strategy as,
Described step 4 is specifically implemented according to following steps：
Step 4.1, target function type (7) (8) (10) (11) calculation procedure 3 shown in Load flow calculation combination step 2 is utilized
The target function value λ of each harmony vector of middle generationj, produced by remembering library searching, 3 kinds of modes of local dip and random selection
The new solution vector (i.e. new harmony) for meeting constraints, and calculate corresponding target function value.Newly the generation rule of harmony is：
In formula, XiFor the feasible zone of HM exogenousd variables, p represents random chance, and HMCR is and sound memory
Library searching probability, chooses the probability of element in data base when representing to generate new harmony.It is each newborn
Into variable all can by parameter PAR carry out judge whether to need local dip, if desired, then lead to
Cross bw(frequency bandwidth bwAdjustment amplitude when representing to be finely adjusted element in new harmony, generally
Before taking 0.01~0.1) it is adjusted：
In formula, PAR is that tone finely tunes probability, and expression is finely adjusted probability to new element, generally takes 0.1 or 0.2, bwFor
Frequency bandwidth, adjustment amplitude when representing to be finely adjusted element in new harmony, generally takes between 0.01~0.1.
Described step 5 is specially：
Utilize the new of the generation of target function type (7) (8) (10) (11) calculation procedure 4 shown in Load flow calculation combination step 2
The target function value λ of harmony, each harmony target function value in the target function value and data base of new harmony is compared, picked
Except the worst solution (i.e. the maximum harmony vector of target function value) in harmony storehouse, while new harmony vector is put into harmony data base
In, if harmony all in harmony storehouse is better than the object function of all harmony vectors of harmony in new harmony, i.e. harmony data base
Value is both less than the target function value of new harmony, then gives up new harmony.
The beneficial effects of the invention are as follows 1) utilize photovoltaic generation and idle fan-out capability, the energy storage device pair of wind-power electricity generation
System voltage is adjusted, and is improved the digestion capability that distribution system generates electricity to clean energy resource, is saved reactive-load compensation equipment
Input；2) compared with the existing universal active distribution system Optimized Operation by target of economy, quality of voltage lifting is added
Index, establish both consider economy more consider the quality of power supply lifting Optimal Operation Model.
Brief description of the drawings
Fig. 1 is the flow chart of the inventive method；
Fig. 2 is that the inventive method is used for the distribution system schematic diagram of example；
Fig. 3 is that the inventive method is used for the load and distributed power source power curve of example；
Fig. 4 (a) is that the inventive method is used for the Optimization Scheduling result of calculation of example；
Fig. 4 (b) is that the inventive method is used for the Optimization Scheduling result of calculation of example；
Fig. 4 (c) is that the inventive method is used for the Optimization Scheduling result of calculation of example；
Fig. 4 (d) is that the inventive method is used for the Optimization Scheduling result of calculation of example；
Fig. 5 (a) is the typical node voltage-contrast figure that the inventive method is used for before and after the Optimized Operation of example；
Fig. 5 (b) is the typical node voltage-contrast figure that the inventive method is used for before and after the Optimized Operation of example.
Embodiment
The present invention is described in detail with reference to the accompanying drawings and detailed description.
A kind of active distribution system Optimization Scheduling for considering voltage deviation, as shown in figure 1, specific according to following steps
Implement：
Step 1, the control variable of the schedulable resource in active distribution system is obtained,
Described schedulable resource includes following several classes, and (1) has the intermittent distributed power source of idle fan-out capability, such as
Wind-power electricity generation power supply, photovoltaic generation power supply etc.)；(2) active adjustable gas turbine power generation power supply；(3) energy storage device.Described
The dominant vector of schedulable scheduling of resource operation is expressed as [QDG1,…,QDGh,PDG1,…,PDGn,PE1,…,PEm]T；
Wherein, preceding h component (QDG1,…,QDGh) represent the idle power output of intermittent distributed power source, middle n
Component (PDG1,…,PDGn) represent gas turbine active power output, last m component (PE1,…,PEm) represent energy storage device
Charge-discharge electric power；
Distributed power source, gas turbine and energy storage device meet following constraint in active distribution system：
Ej(0)=Ej(kΔt) (4)
Above formula (1) and formula (2) represent the active power output and idle power output of distributed power source no more than upper respectively
Lower limit；Formula (3) represents the capacity of energy storage device within zone of reasonableness；Formula (4) represents the charge and discharge of energy-storage units in dispatching cycle
Electric flux conservation；Formula (5) and formula (6) represent that the charge-discharge electric power of energy storage device is not out-of-limit respectively.In formula,WithRespectively
Represent the bound of the active power output of i-th of distributed power source, PDGi(t) i-th of distributed power source having in t is represented
Work(power output；WithThe bound of i-th of idle power output of intermittent distributed power source, Q are represented respectivelyDGi(t)
Represent idle power output of i-th of distributed power source in t；WithJ-th capacity of energy storing device is represented respectively
Bound, Ej(0) primary power of j-th of energy storage device, E are representedj(k Δs t) represents j-th of energy storage device by k scheduling rank
Energy after the discharge and recharge of section, unit scheduling time is Δ t；WithRepresent that the maximum of j-th of energy storage device is filled respectively
Electrical power and the charge power of t periods；WithMaximum discharge power and the t periods of j-th energy storage device are represented respectively
Discharge power.
This step illustrates the control variable of Optimized Operation of the present invention, and the control variable corresponds in step 3 and sound memory storehouse
In parameter.
Step 2, it is considered to security and the aspect factor of economy two, sets up the mathematical modulo of active distribution system Optimized Operation
Type.
Step 2.1, shown in the quality of voltage lifting object function such as formula (7) of foundation, economical operation object function such as formula (8)
It is shown.
In quality of voltage lifting object function (7), Δ U is voltage deviation amount, and K represents the scheduling included in dispatching cycle
Number of stages；N represents node total number；ΔUiFor the voltage deviation amount of the i-th node；δ U are node voltage maximum allowable offset amount,
The calculation formula of function phi is：
In economical operation object function (8), C is operating cost, and Δ T represents the duration of unit scheduling phase；L is represented
The feeder line number that active distribution system is included；K represents the scheduling phase number included in dispatching cycle；N is represented in active distribution system
The gas turbine sum of access；ClAnd P (t)l(t) respectively represent feeder line l t receive bulk power grid electric energy electricity price cost and
The power that bulk power grid is conveyed to feeder line l；CjAnd P (t)DG-j(t) cost of electricity-generating of j-th of gas turbine in t is represented respectively
With active output.
Step 2.2, optimization aim of the invention is Bi-objective, therefore need to be converted into single goal to Bi-objective progress processing.
Using fuzzy set theory method, by the fuzzy membership function formula of linear segmented form, multiple target is converted into total satisfaction
The single-object problem of index.Fuzzy membership function formula and final single object optimization functional expression difference are as follows：
Max λ=min (μ1,μ2) (11)
In fuzzy membership function formula (10), μiFor the degree of membership of sub-goal, wherein, μ1Represent quality of voltage lifting mesh
Target degree of membership, μ2Represent the degree of membership of economical operation target；For the higher limit of specific item scalar functions, i.e. voltage deviation amount and fortune
The higher limit of row expense；fiFor the theoretical optimal objective value of sub-goal, the i.e. theoretially optimum value of voltage deviation amount and operating cost.It is single
In objective optimization functional expression (11), λ represents the degree of membership of total satisfaction, i.e. quality of voltage lifting target and economical operation target
Minimum value.
Active distribution system Optimized Operation mathematical modeling also includes following constraints：
Umin≤Ui≤Umax (14)
Formula (12) represents active distribution system trend constraint；Formula (13) represents active distribution system power-balance constraint；Formula
(14) represent that active distribution system node voltage is not out-of-limit.In formula (12), Pa、QaFor each node active and reactive power, Va、VbPoint
Not Wei node a, b voltage magnitude, Gab、BabFor the conductance between the real and imaginary parts of bus admittance matrix element, i.e. node ab
And susceptance, θabFor the phase angle difference at circuit ab two ends；In formula (13), PDG_iRepresent the active power output of i-th of distributed power source, Plink
Represent that active distribution system interacts power with the electric energy of major network；PLRepresent active power distribution system load；PlossRepresent active power distribution system
System network loss；In formula (14), Umax、UminMaximum, the minimum value of node voltage, U are represented respectivelyiRepresent the voltage of node i.
This step is mainly the mathematical modeling for establishing the inventive method, including object function, constraints and to double
The processing of object function.
Step 3 to step 6 is the mistake of the Optimized Operation mathematical modeling using harmonic search algorithm solution active distribution system
Journey, is introduced in detail below：
Step 3, distribution network parameters are initialized；Initialization algorithm iterations, initial harmony number of vectors scheduling algorithm parameter,
Generate initial harmony storehouse.Harmony data base HM initialization rule is：
In formula,For control variable, idle power output, combustion gas corresponding to the intermittent distributed power source in step 1
The charge-discharge electric power of the active power output of turbine and energy storage device；xi.max, xi.minBe respectively the i-th dimension variable-value most
Greatly, minimum value；Random (0,1) is the random number between 0~1, and j ∈ [1, HMS], HMS represents initial in harmony data base HM
The number of harmony vector.The harmony data base HM of formation is designated as：
In formula,For j-th of scheduling strategy of i-th of distributed power source.H component before the every a line of harmony data base HM
Represent the idle power output Q of intermittent distributed power sourceDG1,…,QDGh, the h+1~h+n representation in components gas turbine have
Work(power output PDG1,…,PDGn, the charge-discharge electric power P of the h+n+1~h+n+m representation in components energy storage deviceE1,…,PEm。
In order that active distribution system is efficiently dissolved clean reproducible energy generate electricity simultaneously make full use of its own well
Reactive-power control capacity adjusting voltage, the active output of wind-power electricity generation and photovoltaic generation is not limited, and regard its idle output as control
Variable processed,
Accessing the control strategy of the blower fan and photovoltaic cells of active distribution system in dispatching cycle is：In formula, QiRepresent that i-th of the idle of scheduling phase is exerted oneself.Gas turbine
With good active regulating power, using the active regulation of gas turbine can reduce the intermittent cleaning energy exert oneself wave zone come
Voltage pulsation problem, while support load normal electricity consumption, using the active output of gas turbine as control variable, access active is matched somebody with somebody
Control strategy of the gas turbine of electric system in dispatching cycle be：
Optimized Operation energy storage device charges when can realize peak, the economic benefit that discharge band is come during paddy, adjusts the charge-discharge electric power of energy storage device
The intermittent cleaning energy can also be reduced and go out voltage pulsation caused by fluctuation and load variations.Need to meet energy storage dress in scheduling process
The conservation of energy put, three constraintss of capacity-constrained and maximum charge-discharge electric power, and energy storage device working condition when different
There is very strong coupling correlation on discontinuity surface, therefore, energy storage device need to be considered within dispatching cycle.Filled with energy storage
Control strategy dispatching cycle that the dump energy put is represented is：
This step is that algorithm obtains data phase, and the initial harmony storehouse of harmonic search algorithm is corresponded into generation access actively matches somebody with somebody
Schedulable resource in electric system goes out force data, including intermittent distributed power source it is idle exert oneself, non-intermittent it is distributed
The active power output of power supply and the charge-discharge electric power of energy storage device.
Step 4, using in target function type (7) (8) (10) (11) calculation procedure 3 shown in Load flow calculation combination step 2
The target function value λ of each harmony vector of generationj.Produced newly by remembering library searching, 3 kinds of modes of local dip and random selection
The solution vector (i.e. new harmony) for meeting constraints, and calculate corresponding target function value.Newly the generation rule of harmony is：
In formula, XiFor the feasible zone of HM exogenousd variables, p represents random chance, and HMCR is harmony data base searching probability, represents
The probability of element is chosen when generating new harmony in data base.Each newly-generated variable can be judged by parameter PAR
Whether local dip is needed, if desired, then pass through bw(frequency bandwidth bwAdjustment when representing to be finely adjusted element in new harmony
Amplitude, before generally taking 0.01~0.1) it is adjusted：
In formula, PAR is that tone finely tunes probability, and expression is finely adjusted probability to new element, generally takes 0.1 or 0.2, bwFor
Frequency bandwidth, adjustment amplitude when representing to be finely adjusted element in new harmony, generally takes between 0.01~0.1.
This step mainly uses harmony vector in the object function that step 2 is set up, the harmony storehouse that calculation procedure 3 is generated
Target function value, new harmony, the contrast iteration for after are then generated by formula (17) and formula (18).
Step 5, produced using target function type (7) (8) (10) (11) calculation procedure 4 shown in Load flow calculation combination step 2
The target function value λ of raw new harmony, each harmony target function value in the target function value and data base of new harmony is carried out
Compare, reject the worst solution (i.e. the maximum harmony vector of target function value) in harmony storehouse, at the same by new harmony vector be put into
In sound memory storehouse, if harmony all in harmony storehouse is better than all harmony vectors of harmony in new harmony, i.e. harmony data base
Target function value is both less than the target function value of new harmony, then gives up new harmony.
Step 6, new harmony vector sum step 5 is generated in repeat step 4, produces new harmony to update by successive ignition
Harmony data base, when renewal reaches maximum iteration, output optimal solution (the minimum harmony of target function value) is that is, optimal to adjust
Degree scheme.
Embodiment
Step 1) from being tested exemplified by IEEE33 Node power distribution systems, as shown in Fig. 2 point in access distribution system
Cloth generator unit and energy storage device totally 10, its type and correspondence parameter are as shown in table 1.Load fluctuation curve and intermittence point
Cloth generator unit change curve of exerting oneself is as shown in Figure 3.This example with 24 hours for dispatching cycle, a length of 15 points during thread
Clock.To bulk power grid purchase electricity price be 490 yuan/(MWh), 283 yuan of gas turbine power generation cost/(MWh).
The distributed generation unit of table 1 and energy storage device configuration
Step 2) parameter of harmonic search algorithm and initial harmony storehouse are set.In order that Optimized model obtains preferable stable
Property and faster convergence rate, the parameter of harmonic search algorithm is set to：Harmony number HMS=20, iterations N=1000,
Harmony data base searching probability HMCR=0.85, tone fine setting probability P AR=0.6, frequency bandwidth bw=0.01.Initial harmony storehouse
HM is configured according to the principle described in the step 3 in algorithm parameter and embodiment.
Step 3) in order to without loss of generality, take node voltage maximum allowable offset amount δ U=0.05.Pass through Load flow calculation knot
The Satisfaction index that step 2 object function tries to achieve each harmony vector in the HM of harmony storehouse is closed, according to the step 4 in embodiment
Described method produces new harmony, then by the rule described in step 5, compares each harmony satisfaction in new harmony and data base
Index, rejects solution poor in harmony storehouse, adjusts harmony data base.
Step 4) according to step 3) it is described calculating is iterated to update harmony data base, when reaching maximum iteration
When export optimal solution, calculate the blower fan tried to achieve it is idle exerts oneself, photovoltaic generation is idle exerts oneself, gas turbine active power output, energy storage list
The scheduling scheme of first discharge and recharge is respectively as shown in Fig. 4 (a)~(d).
Quality of voltage lifting effect to Optimization Scheduling is analyzed, and not optimized system typical node voltage is such as
Shown in Fig. 5 (a), understood with reference to Fig. 3,3 up to 6 when between, due to the access of photovoltaic and wind-power electricity generation, 18 node voltages are high
In upper voltage limit 1.1pu as defined in power distribution network, the access service condition of distributed power source can not be met, influence power consumer
Quality of voltage and the grid-connected of renewable energy power generation are dissolved.When 20 or so, the active power output of distributed power source is not enough, system
Load is laid particular stress on, and part of nodes voltage is less than 0.9pu, and quality of voltage is unqualified.Allusion quotation after Optimized Operation is taken distributed power source
Shown in type node voltage such as Fig. 5 (b), with reference to Fig. 3 as can be seen that at 0~6, because system total load is smaller but wind power plant goes out
Power is larger, and wind energy conversion system works in the idle state of suction, energy storage device and works in charged state to reduce node voltage；It is attached at 18
Closely, system total load is heavier, but wind-force and photovoltaic generation are exerted oneself smaller, and now, wind-force and photovoltaic generation work in and send idle
State, energy storage device work in discharge condition to improve node voltage.In general, after Optimized Operation, each node voltage
It can be maintained within dispatching cycle between 0.94-1.05, quality of voltage has clear improvement, the access operation bar of distributed power source
Part also disclosure satisfy that.
Operating cost reducing effect to Optimization Scheduling is analyzed, and 24 hour operation cost is after Optimized Operation
9597.07 yuan, than using 12060.67 yuan of operating cost during Optimized Operation, not improving 20.4%, indicate optimization and adjust
The optimization that degree method is run to economy is effective.
Claims (6)
1. a kind of active distribution system Optimization Scheduling for considering quality of power supply lifting, it is characterised in that specifically according to following
Step is implemented：
Step 1, the control variable of the schedulable resource in active distribution system is obtained；
Step 2, the mathematical modeling of active distribution system Optimized Operation is set up；
Step 3, distribution network parameters, initialization algorithm iterations, initial harmony number of vectors scheduling algorithm parameter, generation are initialized
Initial harmony storehouse；
Step 4, by remember library searching, local dip and random selection 3 kinds of modes produce the new solution for meeting constraints to
Amount, i.e., new harmony；
Step 5, the worst solution (i.e. the maximum harmony vector of target function value) in harmony storehouse is rejected, while new harmony vector is put
Enter in harmony data base；
Step 6, new harmony vector sum step 5 is generated in repeat step 4, produces new harmony to update harmony by successive ignition
Data base, when renewal reaches maximum iteration, exports the minimum harmony of optimal solution, i.e. target function value, i.e. optimal scheduling
Scheme.
2. the active distribution system Optimization Scheduling according to claim 1 for considering quality of power supply lifting, its feature exists
In in described step 1, the dominant vector of described schedulable scheduling of resource operation is expressed as [QDG1,…,QDGh,PDG1,…,
PDGn,PE1,…,PEm]T；
Wherein, preceding h component (QDG1,…,QDGh) represent the idle power output of intermittent distributed power source, middle n component
(PDG1,…,PDGn) represent gas turbine active power output, last m component (PE1,…,PEm) represent filling for energy storage device
Discharge power.
3. the active distribution system Optimization Scheduling according to claim 1 for considering quality of power supply lifting, its feature exists
In, described step 2 specifically,
Step 2.1, shown in the quality of voltage lifting object function such as formula (7) of foundation, economical operation object function such as formula (8) institute
Show,
In quality of voltage lifting object function (7), Δ U is voltage deviation amount, and K represents the scheduling phase included in dispatching cycle
Number；N represents node total number；ΔUiFor the voltage deviation amount of the i-th node；δ U are node voltage maximum allowable offset amount, function phi
Calculation formula be：
In economical operation object function (8), C is operating cost, and Δ T represents the duration of unit scheduling phase；L represents actively to match somebody with somebody
The feeder line number that electric system is included；K represents the scheduling phase number included in dispatching cycle；N represents what is accessed in active distribution system
Gas turbine sum；ClAnd P (t)l(t) represent that feeder line l receives the electricity price cost and bulk power grid of bulk power grid electric energy in t respectively
The power conveyed to feeder line l；CjAnd P (t)DG-j(t) represent j-th of gas turbine in the cost of electricity-generating of t and active defeated respectively
Go out；
Step 2.2, using fuzzy set theory method, by the fuzzy membership function formula of linear segmented form, multiple target is converted
For the single-object problem of total satisfaction index.Fuzzy membership function formula and final single object optimization functional expression difference
It is as follows：
Max λ=min (μ1,μ2) (11)
In fuzzy membership function formula (10), μiFor the degree of membership of sub-goal, wherein, μ1Represent quality of voltage lifting target
Degree of membership, μ2Represent the degree of membership of economical operation target；For the higher limit of specific item scalar functions, i.e. voltage deviation amount and running cost
Higher limit；fi For the theoretical optimal objective value of sub-goal, the i.e. theoretially optimum value of voltage deviation amount and operating cost.Single goal
In optimal NAND function (11), λ represents total satisfaction, the i.e. degree of membership of quality of voltage lifting target and economical operation target most
Small value；
Active distribution system Optimized Operation mathematical modeling also includes following constraints：
Umin≤Ui≤Umax (14)
Formula (12) represents active distribution system trend constraint；Formula (13) represents active distribution system power-balance constraint；Formula (14)
Represent that active distribution system node voltage is not out-of-limit.In formula (12), Pa、QaFor each node active and reactive power, Va、VbRespectively
Node a, b voltage magnitude, Gab、BabFor the conductance and electricity between the real and imaginary parts of bus admittance matrix element, i.e. node ab
Receive, θabFor the phase angle difference at circuit ab two ends；In formula (13), PDG_iRepresent the active power output of i-th of distributed power source, PlinkRepresent
Active distribution system interacts power with the electric energy of major network；PLRepresent active power distribution system load；PlossRepresent active distribution system net
Damage；In formula (14), Umax、UminMaximum, the minimum value of node voltage, U are represented respectivelyiRepresent the voltage of node i.
4. the active distribution system Optimization Scheduling according to claim 1 for considering quality of power supply lifting, its feature exists
In described step 3 is specifically implemented according to following steps：
Step 3.1, harmony data base HM initialization rule is：
In formula,For control variable, idle power output, gas turbine corresponding to the intermittent distributed power source in step 1
The charge-discharge electric power of active power output and energy storage device；xi.max, xi.minIt is maximum, the minimum of i-th dimension variable-value respectively
Value；Random (0,1) is the random number between 0~1, and j ∈ [1, HMS], HMS represents initial harmony vector in harmony data base HM
Number.
Step 3.2, the harmony data base HM of formation is designated as：
In formula,For j-th of scheduling strategy of i-th of distributed power source.Before the every a line of harmony data base HM between h representation in components
The idle power output Q of having a rest property distributed power sourceDG1,…,QDGh, the active output of the h+1~h+n representation in components gas turbine
Power PDG1,…,PDGn, the charge-discharge electric power P of the h+n+1~h+n+m representation in components energy storage deviceE1,…,PEm；
Step 3.3, control strategy of the blower fan and photovoltaic cells of access active distribution system in dispatching cycle is：i∈[1,h]；In j ∈ [1, HMS], formula, QiRepresent that i-th of the idle of scheduling phase is exerted oneself,
The active output of gas turbine is accessed to control of the gas turbine in dispatching cycle of active distribution system as control variable
Strategy is：i∈[h+1,h+n]；J ∈ [1, HMS],
Represented using the dump energy of energy storage device dispatching cycle control strategy as,i∈[h+n+1,h+n+m]；
j∈[1,HMS]。
5. the active distribution system Optimization Scheduling according to claim 1 for considering quality of power supply lifting, its feature exists
In described step 4 is specifically implemented according to following steps：
Step 4.1, using raw in target function type (7) (8) (10) (11) calculation procedure 3 shown in Load flow calculation combination step 2
Into each harmony vector target function value λj, by remember library searching, local dip and random selection 3 kinds of modes produce it is new
The solution vector (i.e. new harmony) of constraints is met, and calculates corresponding target function value.Newly the generation rule of harmony is：
In formula, XiFor the feasible zone of HM exogenousd variables, p represents random chance, and HMCR is harmony data base searching probability, represents generation
The probability of element is chosen during new harmony in data base.Each newly-generated variable can be judged whether by parameter PAR
Need local dip, if desired, then pass through bw(frequency bandwidth bwAdjustment amplitude when representing to be finely adjusted element in new harmony,
Before generally taking 0.01~0.1) it is adjusted：
In formula, PAR is that tone finely tunes probability, and expression is finely adjusted probability to new element, generally takes 0.1 or 0.2, bwFor bandwidth
Degree, adjustment amplitude when representing to be finely adjusted element in new harmony, generally takes between 0.01~0.1.
6. the active distribution system Optimization Scheduling according to claim 1 for considering quality of power supply lifting, its feature exists
In described step 5 is specially：
The new harmony produced using target function type (7) (8) (10) (11) calculation procedure 4 shown in Load flow calculation combination step 2
Target function value λ, each harmony target function value in the target function value and data base of new harmony is compared, reject and
Worst solution (i.e. the maximum harmony vector of target function value) in sound storehouse, while new harmony vector is put into harmony data base,
If all harmony is better than the target function value of all harmony vectors of harmony in new harmony, i.e. harmony data base all in harmony storehouse
Less than the target function value of new harmony, then new harmony is given up.
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| CN109038653A (en) * | 2018-07-18 | 2018-12-18 | 西安理工大学 | A kind of power distribution network optimizing operation method for pursuing the consumption of distributing wind electricity generating |
| CN110135640A (en) * | 2019-05-15 | 2019-08-16 | 长沙理工大学 | A kind of wind-powered electricity generation distribution Optimization Scheduling improving harmony algorithm based on fuzzy clustering |
| CN110135640B (en) * | 2019-05-15 | 2022-09-13 | 长沙理工大学 | Wind power distribution network optimal scheduling method based on fuzzy clustering improved harmony algorithm |
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| CN113419593B (en) * | 2021-06-29 | 2022-08-23 | 上海电机学院 | Maximum power point tracking method under local shadow condition |
| CN117424294A (en) * | 2023-12-18 | 2024-01-19 | 国网辽宁省电力有限公司经济技术研究院 | Efficient reactive power planning method and system for power distribution network |
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