CN107959307A - A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost - Google Patents
A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost Download PDFInfo
- Publication number
- CN107959307A CN107959307A CN201711284150.XA CN201711284150A CN107959307A CN 107959307 A CN107959307 A CN 107959307A CN 201711284150 A CN201711284150 A CN 201711284150A CN 107959307 A CN107959307 A CN 107959307A
- Authority
- CN
- China
- Prior art keywords
- formula
- power
- risk
- cost
- distribution network
- 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.)
- Pending
Links
- 238000009826 distribution Methods 0.000 title claims abstract description 76
- 238000000034 method Methods 0.000 title claims abstract description 19
- 239000002245 particle Substances 0.000 claims abstract description 73
- 230000003044 adaptive effect Effects 0.000 claims abstract description 20
- 238000010276 construction Methods 0.000 claims abstract description 20
- 230000008859 change Effects 0.000 claims abstract description 18
- 238000005457 optimization Methods 0.000 claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 11
- 238000004364 calculation method Methods 0.000 claims description 28
- 239000007789 gas Substances 0.000 claims description 27
- 238000012423 maintenance Methods 0.000 claims description 21
- 230000005611 electricity Effects 0.000 claims description 11
- 238000009434 installation Methods 0.000 claims description 11
- 238000010248 power generation Methods 0.000 claims description 11
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 4
- KDYAPQVYJXUQNY-OPHDRXFHSA-N 1,2-di-(alpha-linolenoyl)-3-[alpha-D-galactosyl-(1->6)-beta-D-galactosyl]-sn-glycerol Chemical compound O[C@@H]1[C@H](O)[C@@H](O)[C@H](OC[C@@H](COC(=O)CCCCCCC\C=C/C\C=C/C\C=C/CC)OC(=O)CCCCCCC\C=C/C\C=C/C\C=C/CC)O[C@@H]1CO[C@@H]1[C@H](O)[C@@H](O)[C@@H](O)[C@@H](CO)O1 KDYAPQVYJXUQNY-OPHDRXFHSA-N 0.000 claims description 3
- 239000000567 combustion gas Substances 0.000 claims description 3
- 239000012141 concentrate Substances 0.000 claims description 3
- 238000005286 illumination Methods 0.000 claims description 3
- 239000003345 natural gas Substances 0.000 claims description 3
- 230000035515 penetration Effects 0.000 claims description 3
- 239000000243 solution Substances 0.000 claims 13
- 238000002347 injection Methods 0.000 claims 1
- 239000007924 injection Substances 0.000 claims 1
- 238000005516 engineering process Methods 0.000 description 4
- 230000006872 improvement Effects 0.000 description 4
- 238000013439 planning Methods 0.000 description 4
- 230000015556 catabolic process Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- 230000004075 alteration Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000007423 decrease Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000010429 evolutionary process Effects 0.000 description 2
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004146 energy storage Methods 0.000 description 1
- 239000000686 essence Substances 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 238000002360 preparation method Methods 0.000 description 1
- 238000012913 prioritisation Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000012916 structural analysis Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- 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
-
- 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
- H02J3/381—Dispersed generators
-
- H02J3/383—
-
- H02J3/386—
-
- 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]
-
- 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
- H02J3/388—Islanding, i.e. disconnection of local power supply from the network
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of meter and the DG Optimal Configuration Methods of power distribution network operation risk cost, step are as follows；S1, constructs power distribution network operation risk cost model under DG grid-connect modes；S2, constructs operation risk cost model under DG off-grid operation patterns；S3, according to the object function and constraints of step S1 and step S2 construction power distribution network operation risk costs；S4, solves step S3 using particle cluster algorithm is improved, obtains the particle with adaptive optimal control value change rate, the corresponding particle is exactly allocation plan.The present invention establishes the distributed generation resource Optimal Allocation Model for considering power distribution network operation risk cost, it is incorporated into the power networks using DG with risk cost during off-grid operation as object function, power supply allowed capacity, active power balance, node voltage be not out-of-limit in a distributed manner, circuit nonoverload and reversal tidal current be not out-of-limit etc. be constraints DG multiple-objection optimization allocation models, and constructed multi-objective Model is handled using Pareto multiple attributive decision making methods, realize distributing rationally for DG.
Description
Technical field
The invention belongs to distribution network technology field, and in particular to the DG optimizations of a kind of meter and power distribution network operation risk cost are matched somebody with somebody
Put method.
Background technology
Distributed generation resource DG (distribution generation) to optimization energy resource supply structure, reply climate change,
Preserve the ecological environment, promote the sustainable development of socio-economy to have a very important role].With distributed generation technology water
Flat continuous lifting, conventional electric power generation will increasingly have clear superiority with the generation mode that distributed power generation is combined.DG is grid-connected
With reduction system losses and electric grid investment, be conducive to adjust the positive effects such as peak valley, raising system security and reliability, with
This will also bring the problems such as power quality reduction, trend two-way flow, fault current increase, isolated island benefit at the same time.Since DG connects
Enter grid-connected position and partition capacity that the influence to power distribution network is heavily dependent on DG, therefore, in the distribution network planning stage
Consider that DG is distributed rationally to be of great significance.
But in terms of plan model, planned, and lacked with operation risk or the minimum simple target of operating cost more
To the comprehensive consideration of multi objective.Distribution net work structure is complicated, and operation data volume is big, can be more efficient there is an urgent need for finding a kind of optimization algorithm
Optimized model is solved exactly.
The content of the invention
The present invention provides a kind of meter and the DG Optimal Configuration Methods of power distribution network operation risk cost, takes into full account distributed light
Volt power generation and wind power generation output, using the operation risk cost of grid-connect mode and off-network pattern as target, wherein, grid-connect mode
Operation risk cost include power distribution network operating cost and DG fluctuation produce operation risk cost；The operation wind of off-network pattern
Becoming this by inches includes the out-of-limit risk of node voltage, branch overload risk and loses load risk cost, using Pareto multiple attribute decision making (MADM)s
Method handles constructed multi-objective Model, realizes distributing rationally for DG.
In order to solve the above technical problems, the technical solution adopted in the present invention is as follows：
The DG Optimal Configuration Methods of a kind of meter and power distribution network operation risk cost, step are as follows：
S1, constructs power distribution network operation risk cost model under DG grid-connect modes；
Power distribution network operation risk cost includes power distribution network operating cost and operation risk cost；
S1.1, calculates power distribution network annual operating cost Closs；
Concretely comprise the following steps：
S1.1.1, calculates circuit operating cost CL, calculation formula is as follows：
In formula：CpuFor electricity price；τmaxHourage is lost for the annual peak load of i-th branch；ΔPLiFor on i-th branch
Active loss；
S1.1.2, calculates power purchase expense Cen, calculation formula is as follows：
Cen=Tmax×(P∑NEW-P∑DG)×Cpu(2)；
In formula：TmaxFor peak load annual utilization hours；P∑NEWFor load increment；P∑DGContribute for DG；
S1.1.3, calculates the operation and maintenance expenses C of DGDG；
The DG is at least one of wind turbine, photo-voltaic power supply and gas turbine；
When DG is wind turbine, the operation and maintenance expenses of wind turbine only consider equipment investment, operation and maintenance cost, wind turbine year with year cost
Cost CWTCalculation formula be：
In formula：aWTFor the cost of wind turbine；rWT,0For discount rate；mWTFor the fan operation time limit；uWTFor the operation and maintenance expenses of wind turbine
With being generally taken as 5%aWT；NWTFor required wind turbine quantity；
When DG is photo-voltaic power supply, the year cost C of photo-voltaic power supplyPVCalculation formula be：
In formula：aPVFor the cost of photovoltaic；rPV,0For discount rate；mPVFor the photovoltaic motor operation time limit；uPVFor the O＆M of photovoltaic
Expense, is generally taken as 5%aPV；NPVFor required photovoltaic plant quantity；
When DG is gas turbine, the year cost C of gas turbineMTIncluding equipment cost and combustion gas expense, calculation formula is：
CMT=Cf,MT+Ct,MT(5)；
In formula：Cf,MTFor the natural gas expense of system year operation；Ct,MTFor the year investment cost of system equipment；
S1.1.4, with reference to step S1.1.1-S1.1.3, obtains power distribution network annual operating cost Closs；
Closs=CDG+CL+Cen(6)；
In formula：CDGUsed for the operation and maintenance expenses of DG；CLUsed for circuit operation and maintenance expenses；CenFor power purchase expense.
S1.2, calculates power distribution network and considers the operation risk cost C that DG power generation fluctuating characteristics introducerisk, calculation formula is：
Crisk,i=Pr,DGi·PRE,DGi·Cb·ti(7)；
In formula：PRE,DGiContribute for i-th of the expected of DG；CbTo call unit costs needed for stand-by power supply；tiFor i-th of DG
Year run time；Pr,DGiRepresent that i-th of DG with uncertain feature contributes less than the expected probability contributed；
When DG is wind turbine, wind turbine is contributed is less than the expected probability contributed：
In formula：f(PWT) be wind-driven generator output output power probability density, then when wind turbine accesses power distribution network, system is transported
Row risk cost is：
When DG is photo-voltaic power supply, photo-voltaic power supply is contributed is less than the expected probability contributed：
In formula：f(PPV) be photovoltaic output output power probability density, then when photo-voltaic power supply access power distribution network when system operation
Risk cost is：
S1.3, with reference to step S1.1 and step S1.2, obtains power distribution network operation risk cost model under DG grid-connect modes；
In formula：ClossFor the annual operating cost of power distribution network；Crisk,iTo consider the operation risk of DG power generation fluctuating characteristic introducings
Cost；N is the DG number of units with output fluctuation；
S2, constructs operation risk cost model under DG off-grid operation patterns；
Frisk=min (ωS,VRS,V+ωS,LRS,L+ωS,FRS,F) (13)；
In formula：RS,VFor the out-of-limit risk of node voltage；RS,LRisk is overloaded for branch；RS,FTo lose load risk；ωS,VFor
The weight of the out-of-limit risk indicator of node voltage, ωS,LThe weight of risk indicator is overloaded for branch；ωS, F areLoss load risk refers to
Target weight, and ωS,V+ωS,L+ωS,F=1；
S3, constructs the object function and constraints of power distribution network operation risk cost；
S3.1 constructs the object function of power distribution network operation risk cost, and formula is：
F=min (ωCFloss+ωRFrisk) (14)；
In formula：FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system of off-grid operation
Operation risk value；F is the catalogue scale value that DG is distributed rationally, and the final purpose of optimization is to make overall goal function minimum；
S3.2, constructs bound for objective function；
The constraints includes DG constraints, power-balance constraint, node voltage constrains, branch transimission power constrains and anti-
To trend constraint；
S3.2.1, construction DG constraints；
The DG constraints include total capacity, wind-driven generator number, photo-voltaic power supply number and the micro-gas-turbine of DG accesses
Board number；
S3.2.1.1, the constraint of the total capacity of construction DG accesses；
Since wind-power electricity generation and photovoltaic generation are also influenced by wind speed, intensity of illumination, if DG capacity accountings are excessive, Shi Biying
The power quality of acoustic system, therefore the total capacity that DG need to be controlled to access, formula are：
In formula：SDGiFor i-th DG output power；Sl,maxFor power distribution network peak load total amount；η is penetration coefficient；
S3.2.1.2, constructs the constraint of wind-driven generator number, and formula is：
0≤NWT≤NWT,max(16)；
In formula：NWTFor wind turbine number of units；NWT,maxFor the maximum allowable installation number of units of wind turbine；
S3.2.1.3, the number constraint of construction photo-voltaic power supply, formula are：
0≤NPV≤NPV,max(17)；
In formula：NPVFor photo-voltaic power supply number of units；NPV,maxFor the maximum allowable installation number of units of photo-voltaic power supply；
S3.2.1.4, the number of units constraint of construction miniature gas turbine, formula are：
0≤NMT≤NMT,max(18)；
In formula：NMTFor gas turbine number of units；NMT,maxFor the maximum allowable installation number of units of gas turbine；
S3.2.2, constructs power-balance constraint, and formula is：
In formula：PG,iFor node injecting power；PlossFor network loss；PLDFor workload demand；PDGiFor DG injecting powers, due to
The injecting power of wind-driven generator and photovoltaic motor is all stochastic variable, therefore above formula is described as in the form of probability：
In formula：Pr0To meet the probability level of workload demand；
S3.2.3, the constraint of structure node voltage, formula are：
Uimin≤Ui≤Uimax(21)；
In formula：Ui,minFor the upper limit of node i voltage；Ui,maxFor the lower limit of node i voltage；
S3.2.4, the transimission power constraint of construction branch, formula are：
|Pl,i|≤Pl,imax(22)；
In formula：Pl,iFor the transimission power of i-th branch；Pl,imaxFor the transimission power upper limit of i-th branch；
S3.2.5, construction reversal tidal current constraint, formula are：
In formula：PDG,i+QDGiFor the DG injecting powers at node i；PLD,i+QLD,iFor the load power of node i；PL,i+QL,i
The power in circuit downstream is injected for node i；Smax iFor the maximum capacity of circuit i；μ is the limit value that can send power；
S4, solves step S3 using particle cluster algorithm is improved, obtains the particle with adaptive optimal control value change rate,
The corresponding particle is exactly allocation plan；
S4.1, in the case where DG accesses the constraints of power grid total capacity and power grid individual node access capacity, random generation is just
Beginning particle populations；
The scale of population is set as m, then m particle in population population, its structure are：
(a1,a2,…,ai,…,an|b1,b2,…,bi,…,bn) (24)；
In formula：I numbers for metric grid nodes, and i=1 ..., n；biType for the DG accessed at node i, with
Three kinds of wind-driven generator, photo-voltaic power supply and miniature gas burner DG are subjected to calculating processing as different nodes；aiRepresent i-th
The multiple of the unit rated capacity of DG, a are accessed on a nodeiTake [0, PDG,maxi/Pave] integer value in section, PDG,maxiFor
Node i allows the maximum power of access, PaveFor DG unit rated capacities；
S4.2, carries out Load flow calculation, obtains the Pareto optimal solution sets of object function, and calculate in Pareto optimal solution sets
The adaptive value of each particle, chooses optimal particle；
S4.3, according to the speed of current optimal particle more new particle and position；
S4.4, calculates the crowding distance of particle in Pareto optimal solution sets and updates external archival collection；
S4.4.1, calculates and appoints two particle distance d in Pareto optimal solution setsij, calculation formula is：
In formula, FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system of off-grid operation
Operation risk value；To consider the maximum of the system total operating cost of DG operation risk costs；To consider DG operations
The minimum value of the system total operating cost of risk cost；To consider the maximum of the system operation value-at-risk of off-grid operation；To consider the minimum value of the system operation value-at-risk of off-grid operation；
S4.4.2, calculates the crowding distance D of particle i in Pareto optimal solution setsi；
Wherein, d1It is any one particle i in Pareto optimal solution sets and with a distance from its first near particle a, calculates
Formula is：
d1=min { dij|j∈Qset,j≠i} (27)；
d2For any one particle i in Pareto optimal solution sets and with a distance from its second near particle b, calculation formula
For：
d2=min { dij|dij>d1,j∈Qset,j≠i} (28)；
S4.4.3, according to the crowding distance D being calculatediValue, in Pareto optimal solution sets solution carry out by greatly to
Small sequence, rejects the small solution of crowding distance, obtains global maximal solution；
S4.4.4, is saved in external archival by global maximal solution and concentrates；
S4.5, obtains each particle adaptive optimal control value change rate in Pareto optimal solution sets；
S4.6, judges whether adaptive optimal control value change rate is less than threshold value, if being less than threshold value, carries out TSP question and sentences
Disconnected whether iteration is completed, if completing, the corresponding particle of output adaptive optimal control value change rate, the corresponding particle is exactly to configure
Scheme, if not completing iteration, repeat step S4.2-S4.5；If more than threshold value, then judge whether that iteration is completed, if completing,
The corresponding particle of adaptive optimal control value change rate is then exported, the corresponding particle is exactly allocation plan, if not completing iteration, is weighed
Multiple step S4.2-S4.5.
The present invention establishes the distributed generation resource Optimal Allocation Model for considering power distribution network operation risk cost, with the grid-connected fortune of DG
Row is object function with risk cost during off-grid operation, in a distributed manner power supply allowed capacity, active power balance, node electricity
Press not out-of-limit, circuit nonoverload and reversal tidal current is not out-of-limit etc. is the DG multiple-objection optimization allocation models of constraints, and adopt
Constructed multi-objective Model is handled with Pareto multiple attributive decision making methods, realizes distributing rationally for DG.And pass through
Simulation example shows that this method optimizes network structure, reduces operating cost；During failure, it ensure that the power supply of important load can
By property, breakdown loss is reduced, realizes distributing rationally for DG.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is attached drawing needed in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, without creative efforts, may be used also
To obtain other attached drawings according to these attached drawings.
Fig. 1 is the multi-objective particle swarm algorithm flow chart of the present invention.
PG＆E69 Node power distribution system node diagrams when Fig. 2 emulates for the present invention.
Fig. 3 is index comparison diagram before and after the optimization of the invention for emulating an example.
Embodiment
Below in conjunction with the attached drawing in the embodiment of the present invention, the technical solution in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art obtained under the premise of not making the creative labor it is all its
His embodiment, belongs to the scope of protection of the invention.
The DG Optimal Configuration Methods of a kind of meter and power distribution network operation risk cost, are by the power distribution network method of operation point containing DG
It is incorporated into the power networks and off-grid operation both of which for DG：1) it is incorporated into the power networks under pattern, DG provides electricity for load jointly with power distribution network
Energy；2) off-grid operation pattern, that is, distribution network failure state, at this time power distribution network generally powered with radial networks, step is as follows：
S1, constructs power distribution network operation risk cost model under DG grid-connect modes；
Power distribution network operation risk cost includes power distribution network operating cost and operation risk cost；
S1.1, calculates power distribution network annual operating cost Closs；
Concretely comprise the following steps：
S1.1.1, calculates circuit operating cost CL, calculation formula is as follows：
In formula：CpuFor electricity price；τmaxHourage is lost for the annual peak load of i-th branch；ΔPLiFor on i-th branch
Active loss；
S1.1.2, calculates power purchase expense Cen, calculation formula is as follows：
Cen=Tmax×(P∑NEW-P∑DG)×Cpu(2)；
In formula：TmaxFor peak load annual utilization hours；P∑NEWFor load increment；P∑DGContribute for DG；
S1.1.3, calculates the operation and maintenance expenses C of DGDG；
The DG is at least one of wind turbine, photo-voltaic power supply and gas turbine；
When DG is wind turbine, the operation and maintenance expenses of wind turbine only consider equipment investment, operation and maintenance cost, wind turbine year with year cost
Cost CWTCalculation formula be：
In formula：aWTFor the cost of wind turbine；rWT,0For discount rate；mWTFor the fan operation time limit；uWTFor the operation and maintenance expenses of wind turbine
With being generally taken as 5%aWT；NWTFor required wind turbine quantity；
When DG is photo-voltaic power supply, the year cost C of photo-voltaic power supplyPVCalculation formula be：
In formula：aPVFor the cost of photovoltaic；rPV,0For discount rate；mPVFor the photovoltaic motor operation time limit；uPVFor the O＆M of photovoltaic
Expense, is generally taken as 5%aPV；NPVFor required photovoltaic plant quantity；
When DG is gas turbine, the year cost C of gas turbineMTIncluding equipment cost and combustion gas expense, calculation formula is：
CMT=Cf,MT+Ct,MT(5)；
In formula：Cf,MTFor the natural gas expense of system year operation；Ct,MTFor the year investment cost of system equipment；
S1.1.4, with reference to step S1.1.1-S1.1.3, obtains power distribution network annual operating cost Closs；
Closs=CDG+CL+Cen(6)；
In formula：CDGUsed for the operation and maintenance expenses of DG；CLUsed for circuit operation and maintenance expenses；CenFor power purchase expense.
S1.2, calculates power distribution network and considers the operation risk cost C that DG power generation fluctuating characteristics introducerisk, calculation formula is：
Crisk,i=Pr,DGi·PRE,DGi·Cb·ti(7)；
In formula：PRE,DGiContribute for i-th of the expected of DG；CbTo call unit costs needed for stand-by power supply；tiFor i-th of DG
Year run time；Pr,DGiRepresent that i-th of DG with uncertain feature contributes less than the expected probability contributed；
The risk cost of distribution network planning refers to that planning is influenced be subject to expectation factor is difficult in advance so that actual to receive
Benefit has deviated from expected income, thus bears economic loss or obtains the uncertainty of extra returns.Due to DG contribute it is random
Property and fluctuation, when DG contribute do not reach desired value when, be ensure power distribution network still in being run under optimum state, in systems
Corresponding stand-by power supply is called, resulting extra charge is then known as system risk cost.
Operation risk cost CriskCount and risk cost be mainly derived from environment or other factors and influence, when DG contributes
When not reaching desired value, the expense for calling stand-by power supply is exactly operation risk cost Crisk。
When DG is wind turbine, wind turbine is contributed is less than the expected probability contributed：
In formula：f(PWT) be wind-driven generator output output power probability density, then when wind turbine accesses power distribution network, system is transported
Row risk cost is：
When DG is photo-voltaic power supply, photo-voltaic power supply is contributed is less than the expected probability contributed：
In formula：f(PPV) be photovoltaic output output power probability density, then when photo-voltaic power supply access power distribution network when system operation
Risk cost is：
S1.3, with reference to step S1.1 and step S1.2, obtains power distribution network operation risk cost model under DG grid-connect modes；
In formula：ClossFor the annual operating cost of power distribution network；Crisk,iTo consider the operation risk of DG power generation fluctuating characteristic introducings
Cost；N is the DG number of units with output fluctuation；
S2, constructs operation risk cost model under DG off-grid operation patterns；
Frisk=min (ωS,VRS,V+ωS,LRS,L+ωS,FRS,F) (13)；
In formula：RS,VFor the out-of-limit risk of node voltage；RS,LRisk is overloaded for branch；RS,FTo lose load risk；ωS,VFor
The weight of the out-of-limit risk indicator of node voltage, ωS,LThe weight of risk indicator is overloaded for branch；ωS, F areLoss load risk refers to
Target weight, and ωS,V+ωS,L+ωS,F=1.
Power distribution network containing DG is under off-grid operation pattern, and the power grid of trouble point downstream will be disconnected with major network, under trouble point
The DG of trip will continue to provide electric energy to the user area of former dead electricity.In order to maintain the power-balance and power quality inside off-network,
And make off-grid operation DG and energy storage device must cooperate under specified voltage and frequency, it then follows certain control operation
Specification.
When failure occurs, power distribution network is transitioned into by plan off-network or unplanned off-grid operation state by off-network division, from
And realize restoring electricity for power failure load.To realize the bumpless transfer between DG grid-connect modes and off-network pattern, in power distribution network
The possible breakdown state of each selection should preferentially carry out off-network division, and accident is put into contingency set, formulate off-network division
Scheme collection, and then on the basis of the power distribution network under the off-grid operation mode after division, finally obtain Risk of outage loss degree.
S3, constructs the object function and constraints of power distribution network operation risk cost；
S3.1 constructs the object function of power distribution network operation risk cost, and formula is：
F=min (ωCFloss+ωRFrisk) (14)；
In formula：FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system of off-grid operation
Operation risk value；F is the catalogue scale value that DG is distributed rationally, and the final purpose of optimization is to make overall goal function minimum；
S3.2, constructs bound for objective function；
The constraints includes DG constraints, power-balance constraint, node voltage constrains, branch transimission power constrains and anti-
To trend constraint；
S3.2.1, construction DG constraints；
The DG constraints include total capacity, wind-driven generator number, photo-voltaic power supply number and the micro-gas-turbine of DG accesses
Board number；
S3.2.1.1, the constraint of the total capacity of construction DG accesses；
Since wind-power electricity generation and photovoltaic generation are also influenced by wind speed, intensity of illumination, if DG capacity accountings are excessive, Shi Biying
The power quality of acoustic system, therefore the total capacity that DG need to be controlled to access, formula are：
In formula：SDGiFor i-th DG output power；Sl,maxFor power distribution network peak load total amount；η is penetration coefficient；
S3.2.1.2, constructs the constraint of wind-driven generator number, and formula is：
0≤NWT≤NWT,max(16)；
In formula：NWTFor wind turbine number of units；NWT,maxFor the maximum allowable installation number of units of wind turbine；
S3.2.1.3, the number constraint of construction photo-voltaic power supply, formula are：
0≤NPV≤NPV,max(17)；
In formula：NPVFor photo-voltaic power supply number of units；NPV,maxFor the maximum allowable installation number of units of photo-voltaic power supply；
S3.2.1.4, the number of units constraint of construction miniature gas turbine, formula are：
0≤NMT≤NMT,max(18)；
In formula：NMTFor gas turbine number of units；NMT,maxFor the maximum allowable installation number of units of gas turbine；
S3.2.2, constructs power-balance constraint, and formula is：
In formula：PG,iFor node injecting power；PlossFor network loss；PLDFor workload demand；PDGiFor DG injecting powers, due to
The injecting power of wind-driven generator and photovoltaic motor is all stochastic variable, therefore above formula is described as in the form of probability：
In formula：Pr0To meet the probability level of workload demand；
S3.2.3, the constraint of structure node voltage, formula are：
Uimin≤Ui≤Uimax(21)；
In formula：Ui,minFor the upper limit of node i voltage；Ui,maxFor the lower limit of node i voltage；
S3.2.4, the transimission power constraint of construction branch, formula are：
|Pl,i|≤Pl,imax(22)；
In formula：Pl,iFor the transimission power of i-th branch；Pl,imaxFor the transimission power upper limit of i-th branch；
S3.2.5, construction reversal tidal current constraint, formula are：
In formula：PDG,i+QDGiFor the DG injecting powers at node i；PLD,i+QLD,iFor the load power of node i；PL,i+QL,i
The power in circuit downstream is injected for node i；Smax iFor the maximum capacity of circuit i；μ is the limit value that can send power；
S4, solves step S3 using particle cluster algorithm is improved, obtains the particle with adaptive optimal control value change rate,
The corresponding particle is exactly allocation plan；
S4.1, in the case where DG accesses the constraints of power grid total capacity and power grid individual node access capacity, random generation is just
Beginning particle populations；
The scale of population is set as m, then m particle in population population, its structure are：
(a1,a2,…,ai,…,an|b1,b2,…,bi,…,bn) (24)；
In formula：I numbers for metric grid nodes, and i=1 ..., n；biType for the DG accessed at node i, with
Three kinds of wind-driven generator, photo-voltaic power supply and miniature gas burner DG are subjected to calculating processing as different nodes；aiRepresent i-th
The multiple of the unit rated capacity of DG, a are accessed on a nodeiTake [0, PDG,maxi/Pave] integer value in section, PDG,maxiFor
Node i allows the maximum power of access, PaveFor DG unit rated capacities；
S4.2, carries out Load flow calculation, obtains the Pareto optimal solution sets of object function, and calculate in Pareto optimal solution sets
The adaptive value of each particle, chooses optimal particle；
S4.3, according to the speed of current optimal particle more new particle and position；
S4.4, calculates the crowding distance of particle in Pareto optimal solution sets and updates external archival collection；
S4.4.1, calculates and appoints two particle distance d in Pareto optimal solution setsij, calculation formula is：
In formula, FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system of off-grid operation
Operation risk value；To consider the maximum of the system total operating cost of DG operation risk costs；To consider DG operations
The minimum value of the system total operating cost of risk cost；To consider the maximum of the system operation value-at-risk of off-grid operation；To consider the minimum value of the system operation value-at-risk of off-grid operation；
S4.4.2, calculates the crowding distance D of particle i in Pareto optimal solution setsi；
Wherein, d1It is any one particle i in Pareto optimal solution sets and with a distance from its first near particle a, calculates
Formula is：
d1=min { dij|j∈Qset,j≠i} (27)；
d2For any one particle i in Pareto optimal solution sets and with a distance from its second near particle b, calculation formula
For：
d2=min { dij|dij>d1,j∈Qset,j≠i} (28)；
S4.4.3, according to the crowding distance D being calculatediValue, in Pareto optimal solution sets solution carry out by greatly to
Small sequence, rejects the small solution of crowding distance, obtains global maximal solution；
S4.4.4, is saved in external archival by global maximal solution and concentrates；
S4.5, obtains each particle adaptive optimal control value change rate in Pareto optimal solution sets；
S4.6, judges whether adaptive optimal control value change rate is less than threshold value, if being less than threshold value, carries out TSP question and sentences
Disconnected whether iteration is completed, if completing, the corresponding particle of output adaptive optimal control value change rate, the corresponding particle is exactly to configure
Scheme, if not completing iteration, repeat step S4.2-S4.5；If more than threshold value, then judge whether that iteration is completed, if completing,
The corresponding particle of adaptive optimal control value change rate is then exported, the corresponding particle is exactly allocation plan, if not completing iteration, is weighed
Multiple step S4.2-S4.5.
Illustrated below with emulating example.
The present invention is as shown in Figure 2 with the validity of 69 node verification methods of PG＆E, example structure.Improve particle cluster algorithm
Parameter is arranged to：Population scale is 100, maximum iteration tmax=100, maximum inertia weight wmax=0.9, minimum is used
Property weight wmin=0.4, accelerator coefficient c1=c2=2, adaptive optimal control value change rate K=0.1, initial aberration rate pm0=0.4,
The minimum value p that aberration rate allowsm,min=0.1.
Load is 3802+j3694kVA, rated voltage 12.66kV in example.DG maximums access capacity is no more than system
The 25% of total load, then maximum active access capacities of the DG in power grid is 950.5kW.Assuming that the capacity of single DG to be selected is
The integral multiple of 10kW, i.e. unit rated capacity are Pave=10kW, the DG accessed at each node are no more than 250kW, that is, encode
aiThe integer value in [0,25] section is taken, andValue be the integer value in [0,95] section.
Assuming that the installation node to be selected of all types of DG is as shown in table 1 in example, the load level and load of each node load
Weight system is as shown in table 2.
The installation node to be selected of table 1DG
The load level and weight coefficient of 2 node of table
| Load level | Weight coefficient | Node serial number |
| First order load | 500 | 5~8,12~14,47~48,50~51,57,61~67 |
| Two stage loads | 30 | 9~11,15~19,21,30,32~34,39~41,42~45,53~55,58 |
| Three stage loads | 1 | 20th, 22~29,31,35~38,46,49,52,56,59~60,68 |
Contingency set includes 5 faulty lines, as overstriking circuit in Fig. 2, its line failure rate are as shown in table 3.
3 forecast failure collection of table
| Failure is numbered | Faulty line | Probability of malfunction |
| 1 | L15,16 | 1.25×10-4 |
| 2 | L24,25 | 1.74×10-4 |
| 3 | L28,29 | 1.43×10-4 |
| 4 | L46,47 | 1.16×10-4 |
| 5 | L66,67 | 1.56×10-4 |
And analyzed with two schemes.
Scheme one：Miniature gas turbine (MT) and wind-driven generator (WT) hybrid optimization；Scheme two：Miniature gas turbine
(MT) and photo-voltaic power supply (PV) hybrid optimization.
The position of DG and capacity are as shown in table 4 and table 5 after optimization, and desired value after optimization is as shown in figure 3, be
4 scheme of table, one result of study
5 scheme of table, two result of study
From table 4, table 5, from the structural analysis of DG, optimum results based on miniature gas turbine, wind-driven generator and
Photo-voltaic power supply accounting is slightly smaller.In wind-driven generator, photo-voltaic power supply and the overlapping node of gas turbine building site, prioritization scheme institute
The DG of selection is miniature gas turbine rather than wind-driven generator and photo-voltaic power supply, this is because wind-driven generator and photo-voltaic power supply
Randomness is larger, and the fluctuation of output power is larger, there are certain risk cost, to electric network composition improvement not as micro-
Type gas turbine is good.
From figure 3, it can be seen that compared to any DG is not installed when, system operation expense has declined, and operation risk have it is bright
Aobvious decline.It can be seen that reasonable Arrangement DG can improve the network structure of area power grid well so that network operation expense is significantly
Decline, although the uncertainty of wind-driven generator, photo-voltaic power supply adds risk cost, it changes system network architecture
It is kind more obvious.
Table 6 and table 7 count and off-grid operation in the case of DG distribute result rationally and do not count and off-grid operation in the case of DG
Distribute result rationally.
Table 6 is counted and the optimum results of off-grid operation
Table 7 is not counted and the optimum results of off-grid operation
In table 6 and table 7, numeral before bracket is the node serial number of DG on-positions, the digital representation access in bracket
The DG capacity of the node.In the optimum results for considering node off-grid operation, DG access capacities are amounted to and accounted in first order load node
The percentage for optimizing total capacity is 57.7% and 62.9%, this is because first order load weight coefficient is big, caused by causality loss
Much larger than two, three stage loads, when great power grid accident occurs, DG can provide the backup power supply of abundance, can maintain power distribution network
Middle important load power supply, reduces dead electricity loss to the greatest extent.It can be seen that the power distribution network operation risk assessment model for considering off-grid operation is used,
The position of rational planning DG and capacity, can ensure the power supply reliability of important load, effectively in the case of major accident
The loss that reduction accident produces.
From emulation example, present invention optimization network structure, reduces operating cost；During failure, it ensure that important negative
The power supply reliability of lotus, reduces breakdown loss, realizes distributing rationally for DG.
Moreover, of the present invention is to improve multi-objective particle swarm algorithm.
Particle cluster algorithm achieves good effect in many single-object problems, but is asked in processing multiple-objection optimization
During topic, globally optimal solution more than one, optimum results are no longer unique, but a Pareto optimal solution set, it is therefore desirable to it
Certain improvement is carried out, specific improvement is as follows：
1) coding mode
In traditional binary coding scheme, with each node in a binary representation system, this encoding scheme meeting
Make code length long with increasing for node, so as to influence the speed calculated.Therefore, the present invention is encoded using ten
Scheme.
2) selection of globally optimal solution
Assuming that Qset is non-dominant disaggregation, wherein any one particle i (i ∈ Qset) is defined the particle with from it first
Distance closely with the second near particle a and b (i ≠ a, b) is respectively d1And d2, i.e.,：
d1=min { dij|j∈Qset,j≠i}；
d2=min { dij|dij>d1,j∈Qset,j≠i}；
Then crowding distances of the particle i in non-dominant disaggregation Qset is defined as：
For the optimization problem of this paper two targets studied, dijFor：
In formula, Floss、FriskFor two object functions selected by this paper, target isPoint
Maximum and minimum value that Wei be in the target function value.
According to the crowding distance D being calculatediValue, descending sequence is carried out to the solution that non-domination solution is concentrated, is picked
Except the small solution of crowding distance, global maximal solution is obtained.
3) exterior elite archival strategy
An external archival collection is established, the elite particle produced in evolutionary process is preserved, prevents particle cluster algorithm
Algorithm is degenerated in evolutionary process.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
With within principle, any modification, equivalent replacement, improvement and so on, should all be included in the protection scope of the present invention god.
Claims (6)
1. the DG Optimal Configuration Methods of a kind of meter and power distribution network operation risk cost, it is characterised in that step is as follows：
S1, constructs power distribution network operation risk cost model under DG grid-connect modes；
In formula：ClossFor the annual operating cost of power distribution network；Crisk,iTo consider the operation risk cost of DG power generation fluctuating characteristic introducings；
N is the DG number of units with output fluctuation；
S2, constructs operation risk cost model under DG off-grid operation patterns；
Frisk=min (ωS,VRS,V+ωS,LRS,L+ωS,FRS,F) (13)；
In formula：RS,VFor the out-of-limit risk of node voltage；RS,LRisk is overloaded for branch；RS,FTo lose load risk；ωS,VFor node
The weight of voltage limit risk index, ωS,LThe weight of risk indicator is overloaded for branch；ωS, F areLose the power of load risk indicator
Weight, and ωS,V+ωS,L+ωS,F=1；
S3, according to the object function and constraints of step S1 and step S2 construction power distribution network operation risk costs；
S4, solves step S3 using particle cluster algorithm is improved, obtains the particle with adaptive optimal control value change rate, the grain
Corresponding son is exactly allocation plan.
2. the DG Optimal Configuration Methods of meter according to claim 1 and power distribution network operation risk cost, it is characterised in that
In step S1, power distribution network operation risk cost includes power distribution network operating cost and operation risk cost；Concretely comprise the following steps：
S1.1, calculates power distribution network annual operating cost Closs；
Closs=CDG+CL+Cen(6)；
In formula：CDGUsed for the operation and maintenance expenses of DG；CLUsed for circuit operation and maintenance expenses；CenFor power purchase expense；
S1.2, calculates power distribution network and considers the operation risk cost C that DG power generation fluctuating characteristics introducerisk, calculation formula is：
Crisk,i=Pr,DGi·PRE,DGi·Cb·ti(7)；
In formula：PRE,DGiContribute for i-th of the expected of DG；CbTo call unit costs needed for stand-by power supply；tiFor the year of i-th of DG
Run time；Pr,DGiRepresent that i-th of DG with uncertain feature contributes less than the expected probability contributed；
When DG is wind turbine, wind turbine is contributed is less than the expected probability contributed：
In formula：f(PWT) be wind-driven generator output output power probability density, then when wind turbine access power distribution network when system operation wind
Become by inches and be originally：
When DG is photo-voltaic power supply, photo-voltaic power supply is contributed is less than the expected probability contributed：
In formula：f(PPV) be photovoltaic output output power probability density, then when photo-voltaic power supply access power distribution network when system operation risk
Cost is：
S1.3, with reference to step S1.1 and step S1.2, obtains power distribution network operation risk cost model under DG grid-connect modes；
In formula：ClossFor the annual operating cost of power distribution network；Crisk,iTo consider the operation risk cost of DG power generation fluctuating characteristic introducings；
N is the DG number of units with output fluctuation.
3. the DG Optimal Configuration Methods of meter according to claim 2 and power distribution network operation risk cost, it is characterised in that
In step S1.1, concretely comprise the following steps：
S1.1.1, calculates circuit operating cost CL, calculation formula is as follows：
In formula：CpuFor electricity price；τmaxHourage is lost for the annual peak load of i-th branch；ΔPLiFor having on i-th branch
Work(is lost；
S1.1.2, calculates power purchase expense Cen, calculation formula is as follows：
In formula：TmaxFor peak load annual utilization hours；P∑NEWFor load increment；P∑DGContribute for DG；
S1.1.3, calculates the operation and maintenance expenses C of DGDG；
The DG is at least one of wind turbine, photo-voltaic power supply and gas turbine；
When DG is wind turbine, the operation and maintenance expenses of wind turbine year cost only considers equipment investment, operation and maintenance cost, wind turbine year cost
CWTCalculation formula be：
In formula：aWTFor the cost of wind turbine；rWT,0For discount rate；mWTFor the fan operation time limit；uWTUsed for the operation and maintenance expenses of wind turbine, generally
It is taken as 5%aWT；NWTFor required wind turbine quantity；
When DG is photo-voltaic power supply, the year cost C of photo-voltaic power supplyPVCalculation formula be：
In formula：aPVFor the cost of photovoltaic；rPV,0For discount rate；mPVFor the photovoltaic motor operation time limit；uPVUsed for the operation and maintenance expenses of photovoltaic,
Generally it is taken as 5%aPV；NPVFor required photovoltaic plant quantity；
When DG is gas turbine, the year cost C of gas turbineMTIncluding equipment cost and combustion gas expense, calculation formula is：
CMT=Cf,MT+Ct,MT(5)；
In formula：Cf,MTFor the natural gas expense of system year operation；Ct,MTFor the year investment cost of system equipment；
S1.1.4, with reference to step S1.1.1-S1.1.3, obtains power distribution network annual operating cost Closs；
Closs=CDG+CL+Cen(6)；
In formula：CDGUsed for the operation and maintenance expenses of DG；CLUsed for circuit operation and maintenance expenses；CenFor power purchase expense.
4. the DG Optimal Configuration Methods of meter according to claim 1 and power distribution network operation risk cost, it is characterised in that
In step S3, concretely comprise the following steps：
S3.1 constructs the object function of power distribution network operation risk cost, and formula is：
F=min (ωCFloss+ωRFrisk) (14)；
In formula：FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system operation of off-grid operation
Value-at-risk；F is the catalogue scale value that DG is distributed rationally, and the final purpose of optimization is to make overall goal function minimum；
S3.2, constructs bound for objective function；
The constraints includes DG constraints, power-balance constraint, node voltage constraint, the constraint of branch transimission power and reverse tide
Stream constraint.
5. the DG Optimal Configuration Methods of meter according to claim 4 and power distribution network operation risk cost, it is characterised in that
In step S3.2, concretely comprise the following steps：
S3.2.1, construction DG constraints；
The DG constraints include total capacity, wind-driven generator number, photo-voltaic power supply number and the micro-gas-turbine board of DG accesses
Number；
S3.2.1.1, the constraint of the total capacity of construction DG accesses；
Since wind-power electricity generation and photovoltaic generation are also influenced by wind speed, intensity of illumination, if DG capacity accountings are excessive, certainly will influence be
The power quality of system, therefore the total capacity that DG need to be controlled to access, formula are：
In formula：SDGiFor i-th DG output power；Sl,maxFor power distribution network peak load total amount；η is penetration coefficient；
S3.2.1.2, constructs the constraint of wind-driven generator number, and formula is：
0≤NWT≤NWT,max(16)；
In formula：NWTFor wind turbine number of units；NWT,maxFor the maximum allowable installation number of units of wind turbine；
S3.2.1.3, the number constraint of construction photo-voltaic power supply, formula are：
0≤NPV≤NPV,max(17)；
In formula：NPVFor photo-voltaic power supply number of units；NPV,maxFor the maximum allowable installation number of units of photo-voltaic power supply；
S3.2.1.4, the number of units constraint of construction miniature gas turbine, formula are：
0≤NMT≤NMT,max(18)；
In formula：NMTFor gas turbine number of units；NMT,maxFor the maximum allowable installation number of units of gas turbine；
S3.2.2, constructs power-balance constraint, and formula is：
In formula：PG,iFor node injecting power；PlossFor network loss；PLDFor workload demand；PDGiFor DG injecting powers, since wind-force is sent out
The injecting power of motor and photovoltaic motor is all stochastic variable, therefore above formula is described as in the form of probability：
In formula：Pr0To meet the probability level of workload demand；
S3.2.3, the constraint of structure node voltage, formula are：
Uimin≤Ui≤Uimax(21)；
In formula：Ui,minFor the upper limit of node i voltage；Ui,maxFor the lower limit of node i voltage；
S3.2.4, the transimission power constraint of construction branch, formula are：
|Pl,i|≤Pl,imax(22)；
In formula：Pl,iFor the transimission power of i-th branch；Pl,imaxFor the transimission power upper limit of i-th branch；
S3.2.5, construction reversal tidal current constraint, formula are：
In formula：PDG,i+QDGiFor the DG injecting powers at node i；PLD,i+QLD,iFor the load power of node i；PL,i+QL,iFor section
The power in point i injection circuits downstream；Smax iFor the maximum capacity of circuit i；μ is the limit value that can send power.
6. the DG Optimal Configuration Methods of meter according to claim 1 and power distribution network operation risk cost, it is characterised in that
In step S4, concretely comprise the following steps：S4.1, power grid total capacity and the constraints of power grid individual node access capacity are accessed in DG
Under, it is random to generate primary population；
The scale of population is set as m, then m particle in population population, its structure are：
(a1,a2,…,ai,…,an|b1,b2,…,bi,…,bn) (24)；
In formula：I numbers for metric grid nodes, and i=1 ..., n；biType for the DG accessed at node i, by wind
Three kinds of power generator, photo-voltaic power supply and miniature gas burner DG carry out calculating processing as different nodes；aiRepresent i-th of node
The multiple of the unit rated capacity of upper access DG, aiTake [0, PDG,maxi/Pave] integer value in section, PDG,maxiPermit for node i
Perhaps the maximum power accessed, PaveFor DG unit rated capacities；
S4.2, carries out Load flow calculation, obtains the Pareto optimal solution sets of object function, and calculate each grain in Pareto optimal solution sets
The adaptive value of son, chooses optimal particle；
S4.3, according to the speed of current optimal particle more new particle and position；
S4.4, calculates the crowding distance of particle in Pareto optimal solution sets and updates external archival collection；
S4.4.1, calculates and appoints two particle distance d in Pareto optimal solution setsij, calculation formula is：
In formula, FlossTo consider the system total operating cost of DG operation risk costs；FriskTo consider the system operation of off-grid operation
Value-at-risk；To consider the maximum of the system total operating cost of DG operation risk costs；For consider DG operation risks into
The minimum value of this system total operating cost；To consider the maximum of the system operation value-at-risk of off-grid operation；To examine
Consider the minimum value of the system operation value-at-risk of off-grid operation；
S4.4.2, calculates the crowding distance D of particle i in Pareto optimal solution setsi；
Wherein, d1For any one particle i in Pareto optimal solution sets and with a distance from its first near particle a, calculation formula
For：
d1=min { dij|j∈Qset,j≠i} (27)；
d2For any one particle i in Pareto optimal solution sets and with a distance from its second near particle b, calculation formula is：
d2=min { dij|dij>d1,j∈Qset,j≠i} (28)；
S4.4.3, according to the crowding distance D being calculatediValue, descending row is carried out to the solution in Pareto optimal solution sets
Sequence, rejects the small solution of crowding distance, obtains global maximal solution；
S4.4.4, is saved in external archival by global maximal solution and concentrates；
S4.5, obtains each particle adaptive optimal control value change rate in Pareto optimal solution sets；
S4.6, judges whether adaptive optimal control value change rate is less than threshold value, if being less than threshold value, carrying out TSP question and judgement is
No iteration is completed, if completing, the corresponding particle of output adaptive optimal control value change rate, the corresponding particle is exactly allocation plan,
If do not complete iteration, repeat step S4.2-S4.5；If more than threshold value, then judge whether that iteration is completed, if completing, export
The corresponding particle of adaptive optimal control value change rate, the corresponding particle is exactly allocation plan, if not completing iteration, repeat step
S4.2-S4.5。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711284150.XA CN107959307A (en) | 2017-12-07 | 2017-12-07 | A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711284150.XA CN107959307A (en) | 2017-12-07 | 2017-12-07 | A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107959307A true CN107959307A (en) | 2018-04-24 |
Family
ID=61957573
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711284150.XA Pending CN107959307A (en) | 2017-12-07 | 2017-12-07 | A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107959307A (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109473992A (en) * | 2019-01-21 | 2019-03-15 | 国网河北省电力有限公司经济技术研究院 | A kind of method, system and terminal device improving power distribution network toughness |
| CN109672184A (en) * | 2018-12-12 | 2019-04-23 | 国网江西省电力有限公司电力科学研究院 | One kind distribution network voltage control method of photovoltaic containing high proportion and system |
| CN109995076A (en) * | 2018-12-12 | 2019-07-09 | 云南电网有限责任公司电力科学研究院 | A kind of photovoltaic based on energy storage collects system power and stablizes output cooperative control method |
| CN110474367A (en) * | 2019-08-05 | 2019-11-19 | 广东工业大学 | A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss |
| CN110826780A (en) * | 2019-10-24 | 2020-02-21 | 华北电力大学 | Injection upper limit optimization method and system during distributed power supply communication fault |
| CN111313409A (en) * | 2020-03-05 | 2020-06-19 | 国网河北省电力有限公司 | High-proportion controllable source load access petal type power distribution network fault self-healing method and system |
| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
-
2017
- 2017-12-07 CN CN201711284150.XA patent/CN107959307A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109672184A (en) * | 2018-12-12 | 2019-04-23 | 国网江西省电力有限公司电力科学研究院 | One kind distribution network voltage control method of photovoltaic containing high proportion and system |
| CN109995076A (en) * | 2018-12-12 | 2019-07-09 | 云南电网有限责任公司电力科学研究院 | A kind of photovoltaic based on energy storage collects system power and stablizes output cooperative control method |
| CN109672184B (en) * | 2018-12-12 | 2022-03-11 | 国网江西省电力有限公司电力科学研究院 | Photovoltaic power distribution network voltage control method and system |
| CN109473992A (en) * | 2019-01-21 | 2019-03-15 | 国网河北省电力有限公司经济技术研究院 | A kind of method, system and terminal device improving power distribution network toughness |
| CN110474367A (en) * | 2019-08-05 | 2019-11-19 | 广东工业大学 | A kind of micro-capacitance sensor capacity configuration optimization method considering risk of loss |
| CN110474367B (en) * | 2019-08-05 | 2024-01-05 | 广东工业大学 | Micro-grid capacity configuration optimization method considering risk loss |
| CN110826780A (en) * | 2019-10-24 | 2020-02-21 | 华北电力大学 | Injection upper limit optimization method and system during distributed power supply communication fault |
| CN111313409A (en) * | 2020-03-05 | 2020-06-19 | 国网河北省电力有限公司 | High-proportion controllable source load access petal type power distribution network fault self-healing method and system |
| CN111313409B (en) * | 2020-03-05 | 2023-03-24 | 国网河北省电力有限公司 | High-proportion controllable source load access petal type power distribution network fault self-healing method and system |
| CN116404646A (en) * | 2023-06-08 | 2023-07-07 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
| CN116404646B (en) * | 2023-06-08 | 2023-10-20 | 国网江西省电力有限公司电力科学研究院 | Power system scheduling method and system considering standby risk |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107959307A (en) | A kind of DG Optimal Configuration Methods of meter and power distribution network operation risk cost | |
| CN109146706A (en) | A kind of Transmission Expansion Planning in Electric method considering the flexibility equilibrium of supply and demand | |
| CN104361403B (en) | A kind of optimization packet configuration method of distributed power source and micro-capacitance sensor | |
| CN106487005A (en) | A kind of Electric power network planning method considering T-D tariff | |
| CN109948849B (en) | Power distribution network frame planning method considering energy storage access | |
| CN107437824A (en) | A kind of computational methods of the Area distribution formula power supply allowed capacity based on genetic algorithm | |
| CN102510108B (en) | Method for calculating maximum wind power installed capacity of district power network | |
| CN113131468B (en) | Multi-target power distribution network power supply recovery method considering user power failure loss | |
| Zarei et al. | Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations | |
| CN106803130A (en) | Distributed power source accesses the planing method of power distribution network | |
| CN109034587A (en) | A kind of active distribution system Optimization Scheduling for coordinating a variety of controllables | |
| Huang et al. | An optimal operation method of cascade hydro-PV-pumped storage generation system based on multi-objective stochastic numerical P systems | |
| CN112836957B (en) | Regional comprehensive energy system planning method considering power supply reliability | |
| CN113364043A (en) | Micro-grid group optimization method based on condition risk value | |
| Wang et al. | Optimal distribution network expansion planning incorporating distributed generation | |
| Xing et al. | A rolling optimization method of reserve capacity considering wind power frequency control | |
| CN109713720A (en) | A kind of balance of electric power and ener method of new-energy grid-connected operation | |
| CN109472716A (en) | A kind of production capacity end power distribution method multifactor based on the considerations of genetic algorithm | |
| CN110929968A (en) | Comprehensive regulation and control method and system for multi-energy combined supply of smart city | |
| Di et al. | Multi-objective collaborative control scheduling optimization considering wind power grid-connected energy storage access | |
| CN111030191B (en) | Cell power grid planning method based on multi-target cooperation and self-optimization operation | |
| Thorat et al. | Optimization of fuel cost incorporating with wind, solar PV and Electric vehicle energy sources using improved artificial bee colony algorithm | |
| Fan et al. | Research on collaborative planning of power source, grid and load for Shanghai distribution network | |
| CN110350575A (en) | Meter and wind-powered electricity generation receive the transmission system planing method of ability and Demand Side Response | |
| Cheng et al. | Power and energy balance of active distribution network considering operation-control strategy |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180424 |
|
| RJ01 | Rejection of invention patent application after publication |