CN109687510A - A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method - Google Patents
A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method Download PDFInfo
- Publication number
- CN109687510A CN109687510A CN201811507881.0A CN201811507881A CN109687510A CN 109687510 A CN109687510 A CN 109687510A CN 201811507881 A CN201811507881 A CN 201811507881A CN 109687510 A CN109687510 A CN 109687510A
- Authority
- CN
- China
- Prior art keywords
- constraint
- power
- distribution network
- establishes
- node
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 25
- 239000003990 capacitor Substances 0.000 claims description 28
- 238000004146 energy storage Methods 0.000 claims description 23
- 238000005096 rolling process Methods 0.000 claims description 20
- 230000005611 electricity Effects 0.000 claims description 6
- 230000008901 benefit Effects 0.000 claims description 3
- 238000002347 injection Methods 0.000 claims description 3
- 239000007924 injection Substances 0.000 claims description 3
- 239000004744 fabric Substances 0.000 claims 1
- 230000035699 permeability Effects 0.000 abstract description 2
- 238000005457 optimization Methods 0.000 description 12
- 238000009434 installation Methods 0.000 description 2
- 239000000243 solution Substances 0.000 description 2
- 101000813777 Homo sapiens Splicing factor ESS-2 homolog Proteins 0.000 description 1
- 101000712674 Homo sapiens TGF-beta receptor type-1 Proteins 0.000 description 1
- 102100039575 Splicing factor ESS-2 homolog Human genes 0.000 description 1
- 102100033456 TGF-beta receptor type-1 Human genes 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Classifications
-
- H02J3/382—
-
- 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
-
- 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]
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation methods, it include: that step 1) establishes three layers of robust Optimal Operation Model of min-max-min two stages, robust Optimal Operation Model is solved using column constraint generating algorithm (CCG), determines the mode of operation of the slow motion equipment under most severe scene；Short term predicted data of the step 2) based on gray prediction sets objective function, and the mode of operation of the slow motion equipment of constraint condition and determination is run in conjunction with system, establishes the Optimal Operation Model of active distribution network under short-term time scale；Step 3): the ultra-short term prediction data based on gray prediction, comprehensively consider the number of operations of adjustable controllable device and the limitation of operating time, set objective function, the mode of operation of the slow motion equipment of constraint condition and determination is run in conjunction with system, the Optimal Operation Model for establishing active distribution network under ultra-short Time scale can guarantee the area of distributed generation resource high permeability the safety of system very well.
Description
Fields
The invention belongs to power distribution network running optimizatin technical fields, and in particular to when a kind of meter and probabilistic power distribution network are more
Between dimensional optimization operation method.
Background technique
With a large amount of distributed generation resources, deferrable load, controllable resources that reactive power compensator etc. is adjustable access power distribution network, tradition
Power distribution network gradually develop become can be realized generating equipment, energy storage device and electrical equipment coordinated control, it is cleverer
More friendly active distribution network living.It is contemplated that distributed generation resource power output have randomness and fluctuation, precision of prediction compared with
It is low, and predict error as time increases and the features such as increase, higher challenge is proposed to the Optimized Operation of power distribution network, to matching
The safe operation of power grid is put forward higher requirements.How each distributed generation resource active power output of reasonable arrangement, maximally utilizing can be again
Raw energy power output, guarantees the economy of active distribution network operation and safety is critical issue urgently to be resolved.
It is different from traditional power distribution network active power dispatch, it is active due to the resistance of active distribution network and the coupled relation of reactance
Optimization can improve the economy of system by reasonable Optimized Operation, and idle work optimization can reduce network loss and improve system indirectly
Economy.The prediction error for considering renewable energy and load simultaneously, by reducing predetermined period, active distribution network it is fine
Changing dispatching method becomes the emphasis studied in recent years.
Mature has been tended to the Optimized Operation research of active distribution network at present, but for considering probabilistic active
The fining scheduling of power distribution network is still in the exploratory stage.Some scholars use model prediction control only by predetermined period is shortened
The methods of system, although the influence that the randomness that can reduce distributed generation resource power output to a certain extent dispatches power distribution network,
Still it is difficult to provide the power distribution network Optimized Operation scheme under uncertain especially more severe scene.Simultaneously many scholars use with
The method of machine optimization simulates most severe scene using Taka sieve method is covered, but selected scene is difficult to cover all possible scenes.
Thus the key of problem is to establish the active distribution network Multiple Time Scales optimization fortune for considering photovoltaic and negative rules
Row model guarantees so that (including most severe scene) is capable of providing the Optimized Operation scheme of active distribution network under any scene
The economy and safety of system.
Summary of the invention
The present invention exactly be directed to the problems of the prior art, provide it is a kind of meter and the more time rulers of probabilistic power distribution network
Optimizing operation method is spent, by establishing three layers of robust Optimal Operation Model of min-max-min two stages, is generated and is calculated using column constraint
Method solves robust Optimal Operation Model, determines the mode of operation of the slow motion equipment under most severe scene；It is then based on grey
The short term predicted data of prediction is utilized with the minimum objective function of total operating cost in 4 hours futures of system, in conjunction with system
The operation constraint condition that should meet and the mode of operation for determining lower slow motion equipment before, establish under short-term time scale and lead
The Optimal Operation Model of dynamic power distribution network, ultra-short term prediction data of the Real-time Feedback stage based on gray prediction comprehensively consider adjustable
The number of operations of controllable device and the limitation of operating time, it is minimum with the adjustment amount of adjustable controllable device in system ultra-short Time
Objective function, the operation constraint condition that should meet in conjunction with system and a few days ago mode of operation of the slow motion equipment under determination,
Establish the Optimal Operation Model of active distribution network under ultra-short Time scale.
To achieve the goals above, the technical solution adopted by the present invention is that: it is a kind of meter and probabilistic power distribution network it is more when
Between dimensional optimization operation method, include the following steps:
S1, the mode of operation of slow motion equipment determines a few days ago: establishing three layers of robust Optimized Operation of min-max-min two stages
Model solves robust Optimal Operation Model using column constraint generating algorithm, determines the slow motion equipment under most severe scene
Mode of operation；
S2, the Optimal Operation Model in a few days rolling active distribution network under short-term time scale are established: based on the short of gray prediction
Phase prediction data sets objective function, the slow motion that the operation constraint condition that should meet in conjunction with system and step S1 are determined
The mode of operation of equipment establishes the Optimal Operation Model of active distribution network under short-term time scale；
S3, the Optimal Operation Model of active distribution network is established under real-time ultra-short Time scale: based on the ultrashort of gray prediction
Phase prediction data, comprehensively considers the number of operations of adjustable controllable device and the limitation of operating time, sets objective function, in conjunction with being
The mode of operation for the slow motion equipment that the operation constraint condition that meet of uniting and step S1 are determined, establishes ultra-short Time scale
The Optimal Operation Model of lower active distribution network.
As an improvement of the present invention, the slow motion equipment in the step S1 includes at least Loading voltage regulator OLTC
With group switching capacitor group CB.
As another kind improvement of the invention, the foundation of robust Optimal Operation Model further comprises in the step S1:
S11 establishes the objective function for considering the robust Optimized Operation of photovoltaic and negative rules, the objective function
Are as follows:
Wherein,For power distribution network and major network dominant eigenvalues switching cost；WithRespectively gas turbine
DG cost, interruptible load IL and energy storage device ESS cost；WithRespectively compensation capacitor CB and on-load voltage regulation
The cost of compensation of device OLTC；
S12 establishes constraint condition: the constraint condition includes at least the uncertain of power-balance constraint, photovoltaic and load
Property collection constraint, the operation constraint of system security constraint, reactive power compensator SVC, the operation of packet type switched capacitor group CB about
Beam, distributed generation resource related constraint, energy storage constraint, the operation constraint of Loading voltage regulator and the operation of interruptible load constraint.
It is improved as another kind of the invention, in the step S11,
The gas turbine DG costAre as follows:
The interruptible load IL costAre as follows:
The cost of the energy storage device ESSAre as follows:
The cost of compensation of the compensation capacitor CBAre as follows:
The cost of compensation of the Loading voltage regulator OLTCAre as follows:
Wherein, c1,c2,c3It is the cost coefficient of DG；And rCBIt is the cost of compensation system of IL, OLTC and CB respectively
Number；ΔUTWith Δ UCBRespectively the whole day of OLTC gear and CB gear adjusts number, is only adjustable a gear every time；AndFor the gas turbine of connection, can in load, Loading voltage regulator, compensation capacitor and
The node set of energy storage device；NtFor entire dispatching cycle, the Nt=for 24 hours.
As another improvement of the invention, the step S12 further comprises:
S121 establishes power-balance constraint
Wherein: set u (j) is indicated using j as the set of the headend node of the branch of endpoint node；Set v (j) is indicated with j
For the set of the endpoint node of the branch of headend node；WithThe respectively active power and reactive power of t moment ij branch；For the voltage value of t moment j node；For the current value of t moment ij branch；WithThe respectively wattful power of t moment j node
The net injection value of rate and reactive power； AndRespectively represent the load of t moment j node
Active power, ESS charge-discharge electric power, the active power of photovoltaic PV, the active power of gas turbine and having for interruptible load
Function power； AndThe reactive load power that t moment j node is connected respectively, nothing
Reactive power compensation installations SVC compensate power, the reactive power of PV, the reactive power of group switching capacitor CB, gas turbine it is idle
The reactive power of power and energy storage device；rijAnd xijThe respectively resistance of branch ij and reactance；kij,tFor t moment ij branch
The switching gear of the OLTC connected；
S122 establishes the uncertain collection of photovoltaic and load:
Wherein:The respectively predicted value of photovoltaic power output, maximum upper limit deviation and greatest lower bound deviation；The respectively predicted value of load, maximum upper limit deviation and greatest lower bound deviation；For 0-1 change
Amount；
S123 establishes system security constraint
Wherein:WithThe respectively bound of j node voltage amplitude；For the upper limit value of ij branch current；
S124 establishes the operation constraint of reactive power compensator SVC
Wherein:WithThe respectively upper lower limit value of the idle power output of reactive power compensator；
S125 establishes the operation constraint of packet type switched capacitor group CB
Wherein:For the compensation power of every group capacitor；WithThe respectively 0-1 mark of switching operation,
WhenIndicate that t moment j node increases putting into operation for one group of CB,Similarly；For the upper of each switching maximum group number
Limit；For the upper limit of capacitor group switching number；
S126, establishes distributed generation resource related constraint, and the distributed generation resource related constraint includes photovoltaic constraint and miniature
Gas turbine constraint, specific as follows:
S1261, photovoltaic constraint
Wherein:Indicate the predicted value of photovoltaic power output；For photovoltaic DC-to-AC converter peak power output；
S1262, miniature gas turbine constraint
Wherein:For inverter peak power output；For miniature gas turbine Climing constant limit value；
S127 establishes energy storage constraint
Wherein:Indicate the ESS charge of t moment j node；ηchAnd ηdisRespectively efficiency for charge-discharge；WithPoint
Not Wei charge-discharge electric power maximum value；
S128 establishes the operation constraint of Loading voltage regulator
kij,t=kij0+Mij,tΔkij,t
Wherein: Mij,tBy the gear for the OLTC that t moment ij branch connects；It is connected by ij branch
The bound of OLTC gear；kij0For the initial value of gear；Δkij,tFor two adjacent gear positions differences of OLTC；
S129 establishes the operation constraint of interruptible load
Wherein:For the upper limit of j node interruptible load.
It is improved as another of the invention, objective function is in the step S2 with total operation in 4 hours futures of system
The minimum target of cost is realized using 4h as the rolling optimal dispatching in period, it may be assumed that
Wherein:Indicate the contact power, that is, purchase of electricity with major network in a few days rolling stage t moment；With
Respectively indicate the controlled distribution formula power supply and energy storage cost in a few days rolling stage t moment i-node.
As another of the invention improvement, in the step S2 under short-term time scale active distribution network Optimized Operation mould
The constraint condition of type successively includes: step S121- step S124, step S126, step S127 and step S129.
As of the invention with being further improved, the step S3 objective function is controllably set with adjustable in system ultra-short Time
The minimum target of standby adjustment amount, the system ultra-short Time are set as in 5min, realize using 5min as the rolling optimization in period
Scheduling, it may be assumed that
Wherein: U represents the set of Real-time Feedback stage adjustable controllable resources；uFK.real, Δ uFKAnd uDIIt respectively represents in real time
The output valve of the controllable resources of feedback stage, the power output adjusted value of adjustable controllable resources and in a few days rolling stage adjustable controllable resources
Output valve.
As a further improvement of the present invention, in the step S3 under ultra-short Time scale active distribution network optimization tune
The constraint condition for spending model successively includes: step S121- step S124, step S126, step S127 and step S129.
Compared with prior art, a kind of meter proposed by the present invention and the optimization operation of probabilistic power distribution network Multiple Time Scales
Method initially sets up tri- layers of robust Optimal Operation Model of min-max-min and determines slow motion operational state, and base a few days ago
Multiple Time Scales Optimization Solution is realized in short term predicted data and ultra-short term prediction data.Model emphasis proposed by the invention is examined
Consider the uncertain problem of photovoltaic and load, uses boxlike in model and do not know set description uncertainty variable, utilize
CC&G algorithm solves tri- layers of robust Model of min-max-min, guarantees under most severe scene, the more traditional more time rulers of this method
It is more preferable to spend Optimized model economic benefit, while being solved using CC&G algorithm, fast convergence rate, the number of iterations is few.
Secondly, the present invention on the basis of robust Model a few days ago, comprehensively considers system goal function in different dispatching cycles
Difference establishes the fining scheduling model of active distribution network, which is mixed integer linear programming model, can adjust
It is solved with mature solver (such as CPLEX), it is possible thereby to determine the power output shape of adjustable controllable device under most severe scene
State.
In addition, the fining scheduling model established by the present invention for considering probabilistic active distribution network is for distribution
The area of power supply high permeability can guarantee the safety of system very well.
Detailed description of the invention
Fig. 1 is the flow chart of optimizing operation method of the present invention；
Fig. 2 is the system construction drawing in the embodiment of the present invention 1；
Fig. 3 is the purchase electricity price data in the present invention in embodiment 1；
Fig. 4 is last stage day photovoltaic and the load prediction data in the present invention in embodiment 1；
Fig. 5 is in a few days rolling stage photovoltaic and the load prediction data in the present invention in embodiment 1；
Fig. 6 is Real-time Feedback stage photovoltaic and the load prediction data in the present invention in embodiment 1；
Fig. 7 is each adjustable controllable device simulation result diagram in the present invention in embodiment 1.
Specific embodiment
Below with reference to drawings and examples, the present invention is described in detail.
Embodiment 1
Those skilled in the art can understand that unless otherwise defined, all terms used herein (including
Technical terms and scientific terms) there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.
It should also be understood that those terms such as defined in the general dictionary should be understood that have and the context of the prior art
In the consistent meaning of meaning will not be explained in an idealized or overly formal meaning and unless defined as here.
System structure in the present embodiment as shown in Fig. 2, system by photovoltaic (PV1, PV2), gas turbine (MT1, MT2),
Reactive power compensator (SVC1, SVC2), energy storage device (ESS1, ESS2), interruptible load (IL) and group switching capacitor
The composition such as group (CB), the parameter and link position of each equipment are shown in Table 1；Simultaneously under most severe scene the total operating cost of system pair
Than being shown in Table 2；System is connected with power grid, and from power grid power purchase but not to power grid sale of electricity, electricity price data and load data are shown in Fig. 3-6 respectively
It is shown.
Each device parameter in 1 example of table
The comparison of 2 operating cost of table
| This paper model | Traditional multi-time scale model | |
| Totle drilling cost (member) | 32931.2 | 34221.5 |
Present case develops above-mentioned consideration photovoltaic and negative rules using Cplex algorithm packet under Matlab environment
The power output of active distribution network scheduling model, each adjustable controllable device is as shown in Figure 7.
Dimensional optimization operation method when power distribution network based on photovoltaic and negative rules is more, as shown in Figure 1, including as follows
Step:
S1, the mode of operation of slow motion equipment determines a few days ago: establishing three layers of robust Optimized Operation of min-max-min two stages
Model solves robust Optimal Operation Model using column constraint generating algorithm, determines the slow motion equipment under most severe scene
Mode of operation, the slow motion equipment include at least Loading voltage regulator OLTC and group switching capacitor group CB；
The foundation of the robust Optimal Operation Model further comprises:
S11 establishes the objective function for considering the robust Optimized Operation of photovoltaic and negative rules, the objective function
Are as follows:
Wherein,For power distribution network and major network dominant eigenvalues switching cost；WithRespectively gas turbine
DG cost, interruptible load IL and energy storage device ESS cost；WithRespectively compensation capacitor CB and on-load voltage regulation
Device；
The gas turbine DG costAre as follows:
The interruptible load IL costAre as follows:
The cost of the energy storage device ESSAre as follows:
The cost of compensation of the compensation capacitor CBAre as follows:
The cost of compensation of the Loading voltage regulator OLTCAre as follows:
Wherein, c1,c2,c3It is the cost coefficient of DG；And rCBIt is the cost of compensation system of IL, OLTC and CB respectively
Number；ΔUTWith Δ UCBRespectively the whole day of OLTC gear and CB gear adjusts number, is only adjustable a gear every time；AndFor the gas turbine of connection, can in load, Loading voltage regulator, compensation capacitor and
The node set of energy storage device；NtFor entire dispatching cycle, the NtThe cost of compensation of=OLTC for 24 hours；
S12 establishes constraint condition: the constraint condition includes at least the uncertain of power-balance constraint, photovoltaic and load
Property collection constraint, the operation constraint of system security constraint, reactive power compensator SVC, the operation of packet type switched capacitor group CB about
Beam, distributed generation resource related constraint, energy storage constraint, the operation constraint of Loading voltage regulator and the operation of interruptible load constraint, tool
Body is as follows:
S121 establishes power-balance constraint
Wherein: set u (j) is indicated using j as the set of the headend node of the branch of endpoint node；Set v (j) is indicated with j
For the set of the endpoint node of the branch of headend node；WithThe respectively active power and reactive power of t moment ij branch；For the voltage value of t moment j node；For the current value of t moment ij branch；WithThe respectively wattful power of t moment j node
The net injection value of rate and reactive power； AndRespectively represent the load of t moment j node
Active power, ESS charge-discharge electric power, the active power of photovoltaic PV, the active power of gas turbine and having for interruptible load
Function power； AndThe reactive load power that t moment j node is connected respectively, nothing
Reactive power compensation installations SVC compensate power, the reactive power of PV, the reactive power of group switching capacitor CB, gas turbine it is idle
The reactive power of power and energy storage device；rijAnd xijThe respectively resistance of branch ij and reactance；kij,tFor t moment ij branch
The switching gear of the OLTC connected；
S122 establishes the uncertain collection of photovoltaic and load:
Wherein:The respectively predicted value of photovoltaic power output, maximum upper limit deviation and greatest lower bound deviation；The respectively predicted value of load, maximum upper limit deviation and greatest lower bound deviation；For 0-1 change
Amount；
S123 establishes system security constraint
Wherein:WithThe respectively bound of j node voltage amplitude；For the upper limit value of ij branch current；
S124 establishes the operation constraint of reactive power compensator SVC
Wherein:WithThe respectively upper lower limit value of the idle power output of reactive power compensator；
S125 establishes the operation constraint of packet type switched capacitor group CB
Wherein:For the compensation power of every group capacitor；WithThe respectively 0-1 mark of switching operation
Know, whenIndicate that t moment j node increases putting into operation for one group of CB,Similarly；For each switching maximum group number
The upper limit；For the upper limit of capacitor group switching number；
S126, establishes distributed generation resource related constraint, and the distributed generation resource related constraint includes photovoltaic constraint and miniature
Gas turbine constraint, specific as follows:
S1261, photovoltaic constraint
Wherein:Indicate the predicted value of photovoltaic power output；For photovoltaic DC-to-AC converter peak power output；
S1262, miniature gas turbine constraint
Wherein:For inverter peak power output；For miniature gas turbine Climing constant limit value；
S127 establishes energy storage constraint
Wherein:Indicate the ESS charge of t moment j node；ηchAnd ηdisRespectively efficiency for charge-discharge；WithPoint
Not Wei charge-discharge electric power maximum value；
S128 establishes the operation constraint of Loading voltage regulator
kij,t=kij0+Mij,tΔkij,t
Wherein: Mij,tBy the gear for the OLTC that t moment ij branch connects；It is connected by ij branch
The bound of OLTC gear；kij0For the initial value of gear；Δkij,tFor two adjacent gear positions differences of OLTC；
S129 establishes the operation constraint of interruptible load
Wherein:For the upper limit of j node interruptible load.
S2, the Optimal Operation Model in a few days rolling active distribution network under short-term time scale are established: based on the short of gray prediction
Phase prediction data sets objective function, the slow motion that the operation constraint condition that should meet in conjunction with system and step S1 are determined
The mode of operation of equipment establishes the Optimal Operation Model of active distribution network under short-term time scale；
S21 establishes objective function:
Short term predicted data based on photovoltaic and load is realized using 4h as the rolling optimal dispatching in period.Rolling optimization
Objective function is to keep the operating cost in a rolling scheduling period (4h) minimum, it may be assumed that
In formula:Indicate the contact power, that is, purchase of electricity with major network in a few days rolling stage t moment；With
Respectively indicate the controlled distribution formula power supply and energy storage cost in a few days rolling stage t moment i-node.
S22 establishes constraint condition: the condition successively includes: step S121- step S124, step S126, step
S127 and step S129.
S3, the Optimal Operation Model of active distribution network is established under real-time ultra-short Time scale: based on the ultrashort of gray prediction
Phase prediction data, comprehensively considers the number of operations of adjustable controllable device and the limitation of operating time, sets objective function, in conjunction with being
The mode of operation for the slow motion equipment that the operation constraint condition that meet of uniting and step S1 are determined, establishes ultra-short Time scale
The Optimal Operation Model of lower active distribution network, specific as follows:
S31 establishes objective function:
Ultra-short term prediction data based on photovoltaic and load is realized using 5min as the rolling optimal dispatching in period.It considers
The objective function of the operating time of adjustable controllable resources, Real-time Feedback be so that one dispatching cycle adjustable controllable resources power output
Adjustment is minimum:
In formula: U represents the set of Real-time Feedback stage adjustable controllable resources；uFK.real, Δ uFKAnd uDIIt respectively represents in real time
The output valve of the controllable resources of feedback stage, the power output adjusted value of adjustable controllable resources and in a few days rolling stage adjustable controllable resources
Output valve.
S32 establishes constraint condition: the constraint condition successively includes: step S121- step S124, step S126, step
Rapid S127 and step S129.
In the present embodiment, step S1 can distinguish primal problem and subproblem first, subproblem can by Conjugate Search Algorithm and
Greatly _ M algorithm is converted into linear max problem, which is solved by CCG algorithm；It is fine described in step S2 and step S3
Changing scheduling model is MIXED INTEGER non-linearity problems, and mature solver can be used and solved.
So far, it according to the objective function of foundation and the constraint condition of setting, determines under the most severe scene of active distribution network
The real-time power output of various adjustable controllable devices, guarantees the safe and economic operation of system.
In conclusion the embodiment of the present invention initially sets up the active distribution network slow motion equipment that robust Model determines a few days ago
Mode of operation, next is based respectively on the short-term and ultra-short term prediction data of gray prediction, establishes active distribution network fining scheduling
Model.It can be good at coping with the uncertain influence to power distribution network Optimized Operation of renewable energy.
The basic principles, main features and advantages of the present invention have been shown and described above.The technology of the industry
Personnel only illustrate the present invention it should be appreciated that the present invention is not limited by examples detailed above described in examples detailed above and specification
Principle, various changes and improvements may be made to the invention without departing from the spirit and scope of the present invention, these variation and
Improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its is equal
Object defines.
Claims (9)
1. a kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method, which comprises the steps of:
S1, the mode of operation of slow motion equipment determines a few days ago: establishing three layers of robust Optimized Operation mould of min-max-min two stages
Type solves robust Optimal Operation Model using column constraint generating algorithm, determines the behaviour of the slow motion equipment under most severe scene
Make state；
S2, the Optimal Operation Model in a few days rolling active distribution network under short-term time scale are established: short-term pre- based on gray prediction
Measured data sets objective function, the slow motion equipment that the operation constraint condition that should meet in conjunction with system and step S1 are determined
Mode of operation, establish the Optimal Operation Model of active distribution network under short-term time scale；
S3, the Optimal Operation Model of active distribution network is established under real-time ultra-short Time scale: the ultra-short term based on gray prediction is pre-
Measured data comprehensively considers the number of operations of adjustable controllable device and the limitation of operating time, sets objective function, answers in conjunction with system
The mode of operation for the slow motion equipment that the operation constraint condition and step S1 of the satisfaction determine is established main under ultra-short Time scale
The Optimal Operation Model of dynamic power distribution network.
2. a kind of meter as described in claim 1 and probabilistic power distribution network Multiple Time Scales optimizing operation method, feature
It is that the slow motion equipment in the step S1 includes at least Loading voltage regulator OLTC and group switching capacitor group CB.
3. a kind of meter as claimed in claim 1 or 2 and probabilistic power distribution network Multiple Time Scales optimizing operation method, special
Sign is that the foundation of robust Optimal Operation Model in the step S1 further comprises:
S11 establishes the objective function for considering the robust Optimized Operation of photovoltaic and negative rules, the objective function are as follows:
Wherein,For power distribution network and major network dominant eigenvalues switching cost；WithRespectively gas turbine DG at
This, interruptible load IL and energy storage device ESS cost；WithRespectively compensation capacitor CB and Loading voltage regulator OLTC
Cost of compensation；
S12 establishes constraint condition: the constraint condition includes at least the uncertain collection of power-balance constraint, photovoltaic and load
Constraint, the operation of packet type switched capacitor group CB constraint, divides at the operation constraint of system security constraint, reactive power compensator SVC
Cloth power supply related constraint, energy storage constraint, the operation constraint of Loading voltage regulator and the operation of interruptible load constraint.
4. a kind of meter as claimed in claim 3 and probabilistic power distribution network Multiple Time Scales optimizing operation method, feature
It is in the step S11,
The gas turbine DG costAre as follows:
The interruptible load IL costAre as follows:
The cost of the energy storage device ESSAre as follows:
The cost of compensation of the compensation capacitor CBAre as follows:
The cost of compensation of the Loading voltage regulator OLTCAre as follows:
Wherein, c1,c2,c3It is the cost coefficient of DG；And rCBIt is the cost of compensation coefficient of IL, OLTC and CB respectively；Δ
UTWith Δ UCBRespectively the whole day of OLTC gear and CB gear adjusts number, is only adjustable a gear every time；AndFor the gas turbine of connection, can in load, Loading voltage regulator, compensation capacitor and
The node set of energy storage device；NtFor entire dispatching cycle, the Nt=for 24 hours.
5. a kind of meter as claimed in claim 4 and probabilistic power distribution network Multiple Time Scales optimizing operation method, feature
It is that the step S12 further comprises:
S121 establishes power-balance constraint
Wherein: set u (j) is indicated using j as the set of the headend node of the branch of endpoint node；Set v (j) is indicated headed by j
The set of the endpoint node of the branch of end node；WithThe respectively active power and reactive power of t moment ij branch；For
The voltage value of t moment j node；For the current value of t moment ij branch；WithRespectively the active power of t moment j node and
The net injection value of reactive power； AndThe load for respectively representing t moment j node is active
Power, ESS charge-discharge electric power, the active power of photovoltaic PV, the active power of gas turbine and the wattful power of interruptible load
Rate； AndThe reactive load power that t moment j node is connected respectively, idle benefit
Repay device SVC compensation power, the reactive power of PV, the reactive power of group switching capacitor CB, the reactive power of gas turbine
And the reactive power of energy storage device；rijAnd xijThe respectively resistance of branch ij and reactance；kij,tIt is connected by t moment ij branch
OLTC switching gear；
S122 establishes the uncertain collection of photovoltaic and load:
Wherein:The respectively predicted value of photovoltaic power output, maximum upper limit deviation and greatest lower bound deviation；
The respectively predicted value of load, maximum upper limit deviation and greatest lower bound deviation；For 0-1 variable；
S123 establishes system security constraint
Wherein:WithThe respectively bound of j node voltage amplitude；For the upper limit value of ij branch current；
S124 establishes the operation constraint of reactive power compensator SVC
Wherein:WithThe respectively upper lower limit value of the idle power output of reactive power compensator；
S125 establishes the operation constraint of packet type switched capacitor group CB
Wherein:For the compensation power of every group capacitor；WithThe respectively 0-1 mark of switching operation, whenIndicate that t moment j node increases putting into operation for one group of CB,Similarly；For the upper limit of each switching maximum group number；For the upper limit of capacitor group switching number；
S126, establishes distributed generation resource related constraint, and the distributed generation resource related constraint includes photovoltaic constraint and miniature gas
Turbine constraint, specific as follows:
S1261, photovoltaic constraint
Wherein:Indicate the predicted value of photovoltaic power output；For photovoltaic DC-to-AC converter peak power output；
S1262, miniature gas turbine constraint
Wherein:For inverter peak power output；For miniature gas turbine Climing constant limit value；
S127 establishes energy storage constraint
Wherein:Indicate the ESS charge of t moment j node；ηchAnd ηdisRespectively efficiency for charge-discharge；WithRespectively fill
The maximum value of discharge power；
S128 establishes the operation constraint of Loading voltage regulator
kij,t=kij0+Mij,tΔkij,t
Wherein: Mij,tBy the gear for the OLTC that t moment ij branch connects；The OLTC shelves connected by ij branch
The bound of position；kij0For the initial value of gear；Δkij,tFor two adjacent gear positions differences of OLTC；
S129 establishes the operation constraint of interruptible load
Wherein:For the upper limit of j node interruptible load.
6. a kind of meter as claimed in claim 1 or 2 and probabilistic power distribution network Multiple Time Scales optimizing operation method, special
Sign be objective function in the step S2 with system it is 4 hours following in the minimum target of total operating cost, realize with 4h and be
The rolling optimal dispatching in period, it may be assumed that
Wherein:Indicate the contact power, that is, purchase of electricity with major network in a few days rolling stage t moment；WithRespectively
Indicate the controlled distribution formula power supply and energy storage cost that in a few days roll stage t moment i-node.
7. a kind of meter as claimed in claim 5 and probabilistic power distribution network Multiple Time Scales optimizing operation method, feature
The constraint condition for being the Optimal Operation Model of active distribution network under short-term time scale in the step S2 successively includes: step
S121- step S124, step S126, step S127 and step S129.
8. a kind of meter as claimed in claim 1 or 2 and probabilistic power distribution network Multiple Time Scales optimizing operation method, special
Sign is the step S3 objective function with the minimum target of adjustment amount of adjustable controllable device in system ultra-short Time, the system
System ultra-short Time is set as in 5min, realizes using 5min as the rolling optimal dispatching in period, it may be assumed that
Wherein: U represents the set of Real-time Feedback stage adjustable controllable resources；uFK.real, Δ uFKAnd uDIRespectively represent Real-time Feedback
The output valve of the controllable resources in stage, adjustable controllable resources power output adjusted value and in a few days rolling stage adjustable controllable resources it is defeated
It is worth out.
9. a kind of meter as claimed in claim 5 and probabilistic power distribution network Multiple Time Scales optimizing operation method, feature
The constraint condition for being the Optimal Operation Model of active distribution network under ultra-short Time scale in the step S3 successively includes: step
S121- step S124, step S126, step S127 and step S129.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811507881.0A CN109687510B (en) | 2018-12-11 | 2018-12-11 | Uncertainty-considered power distribution network multi-time scale optimization operation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811507881.0A CN109687510B (en) | 2018-12-11 | 2018-12-11 | Uncertainty-considered power distribution network multi-time scale optimization operation method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109687510A true CN109687510A (en) | 2019-04-26 |
| CN109687510B CN109687510B (en) | 2021-05-25 |
Family
ID=66187514
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811507881.0A Active CN109687510B (en) | 2018-12-11 | 2018-12-11 | Uncertainty-considered power distribution network multi-time scale optimization operation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109687510B (en) |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110880771A (en) * | 2019-12-11 | 2020-03-13 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN110994697A (en) * | 2019-12-03 | 2020-04-10 | 国网浙江平阳县供电有限责任公司 | Optimal operation control method and system for alternating current-direct current distribution network containing light storage complex |
| CN111126664A (en) * | 2019-11-25 | 2020-05-08 | 广西电网有限责任公司 | Active power distribution network alternating current power flow management method based on robust optimization |
| CN111612266A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Hydropower cluster delivery net rack optimization method considering clean energy utilization rate |
| CN111612265A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Hydropower cluster delivery planning and operation optimization method considering channel utilization hours |
| CN111612271A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Clean energy delivery planning and operation optimization method considering clean energy utilization rate |
| CN111799847A (en) * | 2020-07-16 | 2020-10-20 | 国网北京市电力公司 | Predictive control method of risk-considering two-stage random model of active power distribution network |
| CN112068431A (en) * | 2020-09-03 | 2020-12-11 | 广州大学 | Control method, system and device for double time scales and storage medium |
| CN113541191A (en) * | 2021-07-22 | 2021-10-22 | 国网上海市电力公司 | Multi-time scale scheduling method considering large-scale renewable energy access |
| CN113555870A (en) * | 2021-07-26 | 2021-10-26 | 国网江苏省电力有限公司南通供电分公司 | Q-learning photovoltaic prediction-based power distribution network multi-time scale optimization scheduling method |
| CN116599060A (en) * | 2023-07-18 | 2023-08-15 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for active power distribution network |
-
2018
- 2018-12-11 CN CN201811507881.0A patent/CN109687510B/en active Active
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111126664A (en) * | 2019-11-25 | 2020-05-08 | 广西电网有限责任公司 | Active power distribution network alternating current power flow management method based on robust optimization |
| CN110994697A (en) * | 2019-12-03 | 2020-04-10 | 国网浙江平阳县供电有限责任公司 | Optimal operation control method and system for alternating current-direct current distribution network containing light storage complex |
| CN110994697B (en) * | 2019-12-03 | 2022-07-26 | 国网浙江平阳县供电有限责任公司 | Optimal operation control method and system for alternating current-direct current distribution network containing light storage combination |
| CN110880771B (en) * | 2019-12-11 | 2022-01-11 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN110880771A (en) * | 2019-12-11 | 2020-03-13 | 广东电网有限责任公司 | Transmission and distribution network reactive power optimization method and device |
| CN111612271B (en) * | 2020-05-28 | 2022-10-14 | 国家电网公司西南分部 | Clean energy delivery planning and operation optimization method considering clean energy utilization rate |
| CN111612271A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Clean energy delivery planning and operation optimization method considering clean energy utilization rate |
| CN111612265A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Hydropower cluster delivery planning and operation optimization method considering channel utilization hours |
| CN111612266A (en) * | 2020-05-28 | 2020-09-01 | 国家电网公司西南分部 | Hydropower cluster delivery net rack optimization method considering clean energy utilization rate |
| CN111612265B (en) * | 2020-05-28 | 2022-10-14 | 国家电网公司西南分部 | Hydropower cluster delivery planning and operation optimization method considering channel utilization hours |
| CN111799847A (en) * | 2020-07-16 | 2020-10-20 | 国网北京市电力公司 | Predictive control method of risk-considering two-stage random model of active power distribution network |
| CN111799847B (en) * | 2020-07-16 | 2022-11-22 | 国网北京市电力公司 | Predictive control method of risk-considering two-stage random model of active power distribution network |
| CN112068431A (en) * | 2020-09-03 | 2020-12-11 | 广州大学 | Control method, system and device for double time scales and storage medium |
| CN113541191A (en) * | 2021-07-22 | 2021-10-22 | 国网上海市电力公司 | Multi-time scale scheduling method considering large-scale renewable energy access |
| CN113555870A (en) * | 2021-07-26 | 2021-10-26 | 国网江苏省电力有限公司南通供电分公司 | Q-learning photovoltaic prediction-based power distribution network multi-time scale optimization scheduling method |
| CN113555870B (en) * | 2021-07-26 | 2023-10-13 | 国网江苏省电力有限公司南通供电分公司 | Q-learning photovoltaic prediction-based power distribution network multi-time scale optimal scheduling method |
| CN116599060A (en) * | 2023-07-18 | 2023-08-15 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for active power distribution network |
| CN116599060B (en) * | 2023-07-18 | 2023-11-21 | 山东大学 | Integrated scheduling method, system, terminal equipment and medium for active power distribution network |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109687510B (en) | 2021-05-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109687510A (en) | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method | |
| Akram et al. | A review on rapid responsive energy storage technologies for frequency regulation in modern power systems | |
| CN105740973B (en) | Intelligent power distribution network comprehensive voltage reactive power optimization method based on mixed integer cone programming | |
| Gu et al. | Mode-adaptive decentralized control for renewable DC microgrid with enhanced reliability and flexibility | |
| CN110690732B (en) | Photovoltaic reactive power partition pricing power distribution network reactive power optimization method | |
| CN109873447B (en) | Multi-time-level active-reactive power regulation and control method for multi-source cooperative active power distribution network | |
| Li et al. | Stability-constrained two-stage robust optimization for integrated hydrogen hybrid energy system | |
| CN109842123B (en) | Phase modulator and layered structure ultra-high voltage direct current receiving end converter station coordinated dynamic reactive power optimization method | |
| CN107332252B (en) | Comprehensive low-voltage treatment method for power distribution network considering generalized reactive power source | |
| CN108711868A (en) | It is a kind of meter and islet operation voltage security GA for reactive power optimization planing method | |
| KR102133897B1 (en) | Method for controlling a voltage and reactive power based on real time measurement, program for the same | |
| CN108539797A (en) | A kind of secondary frequency of isolated island micro-capacitance sensor and voltage control method considering economy | |
| Farooq et al. | Power generation control of restructured hybrid power system with FACTS and energy storage devices using optimal cascaded fractional‐order controller | |
| CN106385044A (en) | Composite energy storage control system used for wind power plant power generation plan tracking and control method thereof | |
| CN110247404A (en) | Wind-electricity integration voltage hierarchical coordinative control method, system, medium and equipment | |
| Nour et al. | Automatic generation control of a future multisource power system considering high renewables penetration and electric vehicles: Egyptian power system in 2035 | |
| CN105826946A (en) | Power distribution network dynamic reactive power optimization method for large-scale photovoltaic access | |
| Kandasamy et al. | Deep CNN–LSTM-based DSTATCOM for power quality enhancement in microgrid | |
| Zhu et al. | $\mu $-Synthesis Robust Control of Variable Speed Wind Turbine Generators for Participating in Microgrid Frequency Regulation | |
| Au-Yeung et al. | Development of a voltage and frequency control strategy for an autonomous LV network with distributed generators | |
| Wadi et al. | Optimization of Distributed Generation Using Homer Software and Fuzzy Logic Control | |
| CN109193634A (en) | Island operation of power networks optimization method and system based on multiterminal flexible direct current | |
| Omar et al. | A dynamic switched compensation scheme for grid-connected wind energy systems using cuckoo search algorithm | |
| AL-Maaitah et al. | Particle Swarm Optimizer for BESS Operation to Mitigate Voltage Deviation of the Modified IEEE 9-bus Power System | |
| Badrudeen et al. | Low-inertia control of a large-scale renewable energy penetration in power grids: A systematic review with taxonomy and bibliometric analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20201202 Address after: 213300 room 428, building a, 218 Hongkou Road, Kunlun Street, Liyang City, Changzhou City, Jiangsu Province (in Zhongguancun Science and Technology Industrial Park, Jiangsu Province) Applicant after: Liyang Research Institute of Southeast University Applicant after: SOUTHEAST University Address before: Four pailou Nanjing Xuanwu District of Jiangsu Province, No. 2 210096 Applicant before: SOUTHEAST University |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant |