CN105703369A - Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network - Google Patents
Multi-target random fuzzy dynamic optimal energy flow modeling and solving method for multi-energy coupling transmission and distribution network Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention relates to a multi-target random fuzzy dynamic optimal energy flow modeling and solving method for a multi-energy coupling transmission and distribution network and belongs to the field of day-ahead scheduling plan research of electric power systems in an energy interconnection environment. The method comprises the following steps: basic data in a system scheduling period are obtained,; random fuzzy space-time sequence models for large-scale wind power, distributed power source and multi-energy loads are obtained via historical data mining; power and voltages of a power transmission network and all active distribution networks at joint nodes are used as share variables; multi-target SoS dynamic optimal energy flow models characterized by high economic performance, low carbon emission, renewable energy absorption, loss reduction and the like are built within static state security constraints; multi-energy source charge forecast can be realized through random fuzzy simulation; a Pareto solution set, an optimal compromise solution and an energy flow result can be obtained via adoption of an improved SoS layered optimizetion algorithm based on approximate dynamic programming and NSGA-11. The method can adapt to a development trend of energy interconnection, and comprehensive coordination optimization of day-ahead scheduling of transmission and distribution parties can be realized on the premise that requirements for static state safety and stabilization of systems can be satisfied.
Description
Technical field
The invention belongs to the lower power system operation plan research field a few days ago of energy interconnection, relate to a kind of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream modeling and method for solving。
Background technology
Under energy Background of Internet, actively distribution (activedistributionnetwork, ADN) in, the development of distribution type renewable energy and energy hinge (energyhub, EH) contributes to realizing energy-saving and emission-reduction and the utilization of multiple-energy-source comprehensive high-efficiency。Lotus side, the source power of multi-energy system injects and has uncertain and obvious space and time difference, and conventional scheduling method being forbidden, distribution is to power transmission network (transmissionsystem, TS) power is sent, cause that part regenerative resource is difficult to on-site elimination and is cut in, thus actively distribution is inexorable trend with the feeder line power of power transmission network alternately。Based on this, making full use of TS is carrier, cooperative scheduling TS and each ADN under security constraint, thus promoting that the space-time of comprehensive energy in wide scope is complementary, multiple-energy-source comprehensive utilization and regenerative resource is dissolved and is had the certain significance。
Often with power transmission network or distribution respectively object of study in conventional scheduling model, distribution equivalence is PQ load bus when analyzing by power transmission network, be infinitely great power supply by power transmission network equivalence when distribution is analyzed, and be left out power transmission network and distribution competition and actively the internal multiple-energy-source of distribution couple。Existing scholar is conceived to integrated scheduling problem research defeated, distribution in recent years, based on to moral this decompose, systems engineering theory (systemofsystems, SoS) etc., describe it as composition decomposition Unit Combination Optimized model and solved by iteration optimizing algorithm。The multiple-energy-sources such as electricity, air and heat carry out coupling by EH and convert and supply, have scholar to carry out corresponding EH modeling and scheduling research for this。Also the phenomenon having scholar to be interconnected by EH coupling based on natural gas grid and power network, be have studied the analysis method of multiple-energy-source mixed tensor stream, and analyzes network energy interactive information feature。
To sum up, the multiple target Random-fuzzy dynamic optimal energy flow model of the lower transmission & distribution net of research energy interconnection and method for solving thereof, dissolve and the multiobjective optimization such as line loss synthesizing and coordinating transmission & distribution each side producing cost, dusty gas discharge, regenerative resource under meeting static system safety and stability premise, rarely have report in the prior art, be but a necessary research contents of power system dispatching method a few days ago under wide area energy interconnection background。
Summary of the invention
Solve the technical problem that: for the deficiencies in the prior art, the present invention " a kind of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream modeling and method for solving ", propose the phenomenon of the multiple uncertain injection of electric power system source lotus side multiple-energy-source under energy interconnection, build the multiobjective optimization energy flow model of multipotency coupling actively distribution and power transmission network integrated system based on SoS thought and study its solution procedure method, it is intended to interconnecting the dispatching method of dissolving of wide area energy space-time complementation under background for the energy and theoretical reference is provided。
Technical scheme: a kind of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream modeling and method for solving, the method comprises the steps:
Step 1: obtain system master data within dispatching cycle, and excavate acquisition scale wind power output, distributed power source by historical data and exert oneself and the Random-fuzzy Time-space serial model of multiple-energy-source load etc.。
Step 2: with power in common node set of TS and each ADN and voltage for shared variable, builds and takes into account economy under Static Security Constraints, low-carbon (LC), regenerative resource are dissolved, dropped the multiobject SoS dynamic optimal energy flow models such as damage。
Step 3: obtain multipotency lotus Time-space serial predictive value in a steady stream by Random-fuzzy simulation, judge the different operational modes of ADN, adopt the improvement SoS hierarchy optimization algorithm based on approximate dynamic programming Yu NSGA-II, obtain Pareto disaggregation, optimal compromise solution and corresponding energy stream result。
Beneficial effect: the present invention adapts to multiple-energy-source and accesses development trend, under meeting static system safety and stability premise, effectively realizes transmission & distribution each side and dispatches comprehensive coordination optimization a few days ago。
Accompanying drawing explanation
Fig. 1: one multipotency of the present invention coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream modeling and method for solving implementing procedure；
Fig. 2: the example energy resource system schematic diagram of the present invention；
Fig. 3: the derivation algorithm flow chart of the multiple target Random-fuzzy dynamic optimal energy stream of the present invention。
Detailed description of the invention
Below in conjunction with drawings and the specific embodiments, the present invention is further described。
A kind of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream that the present invention proposes models and method for solving, and its whole implementation flow process is shown in Fig. 1, its row is described in detail with certain energy resource system for specific embodiment below, and Fig. 2 is shown in by its schematic diagram。Embodiment is used for illustrating but is not limited to the present invention。
Step 1: obtain system master data within dispatching cycle, and excavate acquisition scale wind power output, distributed power source by historical data and exert oneself and the Random-fuzzy Time-space serial model of multiple-energy-source load etc.。
Obtain Basic Topological within dispatching cycle of TS, each ADN, natural gas grid, branch impedance, fired power generating unit exert oneself the essential informations such as limit and data；Excavate and distributed constant matching based on to the wind speed of different time/region, intensity of illumination, electricity/gas/thermic load historical data, it is thus achieved that Random-fuzzy Time-space serial model and chance measure function representation scale wind energy turbine set, distributed power source, multiple-energy-source load etc.。
Step 2: with power in common node set of TS and each ADN and voltage for shared variable, builds and takes into account economy under Static Security Constraints, low-carbon (LC), regenerative resource are dissolved, dropped the multiobject SoS dynamic optimal energy flow models such as damage。
First refer mainly to generation, variable and the subscript etc. occurred in model is described as follows:
T power transmission network (TS)；
D is distribution (ADN) actively；
H energy hinge (EH)；
The t period；
ITS node serial number；
JADN internal node is numbered；
K natural gas grid node serial number；
ΦTTS node set；
ΦT-GWith the node set of fired power generating unit in TS,
ΦT-tDWith tradition distribution connected node set in TS,
ΦT-DWith ADN connected node set in TS,
ΦDADN internal node set；
ΦD-HADN inside and natural gas grid connected node, i.e. EH node set；
xtTS local decision variable vector, thermoelectricity is meritorious exerts oneself, voltage, wind electricity digestion power etc.；
ytADN local decision variable vector, i.e. its lower power of dissolving of DG, EH node natural gas influx, electrical power influx and operational mode parameter etc.；
ztTS and ADN shared variable vector zt={ (PLi,t,Ui,t)|i∈ΦT-D, namely connect the active power of node, voltage, be the subset of decision variable。
A kind of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy flow model that the present invention builds is as follows:
(1) upper strata: TS Optimized model
1) object function:
A) economic goal
The economic goal of TS can be described as to maximize purchases/the interests of sale of electricity link, and purchasing behavior includes from thermal power plant and the ADN power purchase that can send power, and sales behavior includes to as the ADN of load and tradition distribution sale of electricity, and the cost of wind energy turbine set additionally considers。
Wherein: ai、bi、ciFor being positioned at the generating set cost of electricity-generating parameter of node i；PGi,tExerting oneself for being positioned at the meritorious of the generating set of node i, flowing into node is just；PLi,tFor being positioned at the Distribution Network Load Data power of node i, flowing out node is just, namely flowing to distribution from power transmission network is positive direction, then when ADN is as power supply, and PLi,t< 0, above formula Section 2 is taken into account after negative sign as just, represents that TS is from this ADN side's power purchase；ξiWith ρiRespectively represent ADN with tradition distribution sell (purchasing) electricity charge use, be positive number,
B) low-carbon (LC) target
It is described as fired power generating unit CO2Discharge minimizes。
Wherein: αi、βi、γiFor being positioned at the generating set CO of node i2Discharge parameter。
C) regenerative resource is dissolved target
Be described as scale wind energy turbine set exert oneself dissolve maximum。
Wherein:Represent that wind energy turbine set prediction is exerted oneself, PWG,tActual exert oneself for it。
D) damage target drops
The network active loss being described as TS is minimum。
Wherein: Ui,tRepresent the voltage of node i；Bi represents the node being connected with node i, and its set is ΓT, Ubi,tRepresent the voltage of node bi；Gi,bi,tWith Bi,bi,tRepresent that node i is connected conductance and the susceptance of branch road with node bi respectively；δi,bi,tRepresent phase angle difference。
2) constraints:
A) power-balance constraint
For TS is total to interlink point (i.e. shared variable z with ADNtRelate to node):
Other nodes for TS:
Formula (5) and (6) are distinctive in that, P in formula (5)Li,tWith Ui,tIt it is a part for TS decision variable。
B) node voltage bound constraint
C) branch road through-put power constraint
D) TS and ADN connects feeder line power constraint
E) fired power generating unit constraint
Exert oneself bound, idle bound of exerting oneself, Climing constant including generated power。
F) system spinning reserve
By system peak loadCertain percentage ratio υ consider system spinning reserve, fired power generating unit undertake。
G) node static security margin constraint
Wherein, (QLi,t) be load or burden without work it is QLi,tTime collapse of voltage point, Ai、Bi、CiThe respectively P-Q-V static voltage stability boundary expressions coefficient of node i,Being maximum load margin, λ is engine sta bility margin。
(2) lower floor: ADN Optimized model (be positioned at TS node i ADN for object of study)
1) object function:
A) economic goal
It is minimum that the economic goal of ADN is described as power purchase expense, including from major network and the power purchase expense from its internal distributed energy。
Wherein, ξDG,jRepresent the ADN electricity price from distributed energy power purchase, PDG,j,tRepresent that distributed energy is exerted oneself。
About ADN operational mode and determine will step 3 algorithm set forth in discuss in detail。
B) low-carbon (LC) target
The CO of the cogeneration unit gas turbine being described as under ADN EH node2Discharge capacity is minimum。
Wherein eEHRepresent the CO of the cogeneration unit gas turbine of EH node2Emission factor, CgeRepresent that natural gas energy resource is converted into the transformation ratio of electrical power, Pgj,tRepresent natural gas influx
C) regenerative resource is dissolved target
Be described as the wind/light distribution type renewable energy of ADN exert oneself dissolve maximum。
Wherein, PDGj,tWithThe respectively actual online power of distributed power source and pre-power scale。
D) damage target drops
The network active loss being described as ADN is minimum。
2) constraints:
A) ADN node power Constraints of Equilibrium
For ADN is total to interlink point (i.e. shared variable z with TStRelate to node that is ADN bus):
Wherein PESSj,tFor the power of energy storage device, using it as load charged state for just。Then formula (17) means: in ADN, all electrical power load sums add all energy storage device power and all network loss, deduct all distributed energies and exert oneself, and obtains bus exchange power PLi,t, its positive and negative operational mode determining ADN。
For EH node, PLj,tThe actual electrical power represented from electrical network inflow EH, this node natural gas energy resource flows into as Pgj,t, other energy such as distributed energy flows into and isElectric load under EH isNatural gas load isThermic load isWherein subscript "～" represent that this variable is Random-fuzzy Uncertainty。Then the multiple-energy-source power-balance constraint representation of EH node is
For other nodes of ADN:
B) ADN and TS connects feeder line power constraint
C) ADN node voltage constraint
D) natural gas grid node flow Constraints of Equilibrium
Wherein
It it is the pipe natural gas flow between two natural gas node k and bk；
E) natural gas grid node pressure constraint
F) natural gas grid pipeline flow constraint
G) energy storage device constraint
It is positioned at the energy storage device of node j, the constraint of its charge-discharge electric power and state of charge constraint etc. should be met when traffic control。
Step 3: obtain multipotency lotus Time-space serial predictive value in a steady stream by Random-fuzzy simulation, judge the different operational modes of ADN, adopt the improvement SoS hierarchy optimization algorithm based on approximate dynamic programming Yu NSGA-II, obtain Pareto disaggregation, optimal compromise solution and corresponding energy stream result。
First institute's established model in step 3 is converted。At upper strata TS Optimized model, by shared variable ztIt is expressed as the local variable η that upper strata optimizest, in lower floor's ADN Optimized model, by ztIt is expressed as local variable μt, hereby it is ensured that ηt=μtThe optimum results that can make TS and ADN is consistent。Definition slack variable ct=ηt-μtAnd penalty function fπ(ct)=σ ct, wherein σ is along with iterations increases the dynamic penalty coefficient being gradually increased, by fπ(ct) be attached on each object function。Bi-level optimal model decoupling is so made to optimize and constrained each other, by iteration until ct< ε, it is thus achieved that composition decomposition optimization solution。
Algorithm flow is as it is shown on figure 3, and be described below。
1) Random-fuzzy simulation method is adopted to produce the Time-space serial of scale output of wind electric field, distributed wind/light, each node multiple-energy-source load；
2) ectonexine iterations zero setting, i.e. l=0, w=0, Initialize installation penalty coefficient σ respectively；
3) for making ADN give full play to the self-centered coordination ability and realize dissolving of its internal distribution type renewable energy, first the multiple target dynamic optimal energy flow problem of lower floor ADN is solved, obtain now corresponding μ according to optimum resultst(0)={ (PLi,t,Ui,t)|i∈ΦT-D, PLi,tPositive/negative/load/three kinds of power supply/isolated island operational mode of the other corresponding A DN of zero, if load/electric source modes, then turn 5), if island mode, then turn 4)；
4) island mode correspondence disconnects TS and ADN interconnection tie, the full decoupled operation of TS and ADN, and its independent optimization is solved, and turns 9)；
5) w=w+1, with min{fn[xt,ηt(w)]+fπ[ηt(w)-μt(w-1)] }, n=1,2,3,4, solve upper strata TS multiple target Dynamic Optimal Power Flow Problem, obtain ηt(w)；
6) with min{fn[yt,μt(w)]+fπ[ηt(w)-μt(w)] }, n=1,2,3,4, solve lower floor ADN multiple target dynamic optimal energy stream, obtain μt(w)；
7) judge whether to meet internal layer iteration convergence condition ηt(w)-ηt(w-1) < ε and μt(w)-μt(w-1) < ε, if it is not, then turn 4), if so, then turn 8)；
8) judge whether to meet external iteration condition of convergence ηt(w)-μtW () < ε, if it is not, then l=l+1, updates penalty coefficient σ, goes to step 4), if so, then turn 9)；
9) output optimization solution and energy stream result, algorithm terminates。
In above-mentioned flow process:
(1) step 3) in, for making ADN give full play to the self-centered coordination ability and realize dissolving of its internal distribution type renewable energy, three kinds of operational modes during ADN is optimized and determining are set forth as follows。
After the multiple-energy-source load spatio-temporal prediction sequence that Random-fuzzy simulation produces, when ADN optimizes, first calculate the formula (19) under now state, if PLi,t< 0, illustrate that the internal distributed energy generating of ADN has more than needed, then first consider to call energy storage device charging storage, under the premise meeting security constraint, if still having more than needed after fully calling energy storage device, then to TS power transmission under the premise meeting safety and bus power constraints, now ADN is power supply status；If initial pLi,t> 0, illustrates that ADN can not supply by teaching display stand, then first consider to call energy storage device discharge supply, if still cannot be self-sufficient after fully calling, then absorb electrical power from TS, and ADN runs on load condition；If P can be made under original state or by calling energy storage deviceLi,t=0, then it is isolated island depending on ADN, with TS decoupling running optimizatin。In above process, dissolve completely if cannot realize regenerative resource under constraints, then the means reduction part being considered as active management is exerted oneself。
(2) the multiple target dynamic optimal energy stream method for solving adopted is the multiobjective optimization energy flow algorithm based on approximate dynamic programming Yu NSGA-II。
Wherein, NSGA-II is adopted to ask for Pareto optimal solution set, Pareto is solved to each non-domination solution concentrated, Fuzzy satisfaction computing formula less than normal is adopted to calculate the satisfaction of each of which desired value, calculate the comprehensive satisfaction of each non-domination solution again, selected the path of cumulative Maximum Satisfaction by the approximate dynamic programming of Policy iteration, constitute the solution of multiple target dynamic optimal energy stream。
Embodiment of above is merely to illustrate the present invention; and it is not limitation of the present invention; those of ordinary skill about technical field; without departing from the spirit and scope of the present invention; can also making a variety of changes and modification, therefore all equivalent technical schemes fall within the protection category of the present invention。
Claims (6)
1. the modeling of multipotency coupling transmission & distribution net multiple target Random-fuzzy dynamic optimal energy stream and a method for solving, is characterized in that the method comprises the steps:
Step 1: obtain system master data within dispatching cycle, and excavate acquisition scale wind power output, distributed power source by historical data and exert oneself and the Random-fuzzy Time-space serial model of multiple-energy-source load etc.；
Step 2: with power in common node set of TS and each ADN and voltage for shared variable, builds and takes into account economy under Static Security Constraints, low-carbon (LC), regenerative resource are dissolved, dropped the multiobject SoS dynamic optimal energy flow models such as damage；
Step 3: obtain multipotency lotus Time-space serial predictive value in a steady stream by Random-fuzzy simulation, judge the different operational modes of ADN, adopt the improvement SoS hierarchy optimization algorithm based on approximate dynamic programming Yu NSGA-II, obtain Pareto disaggregation, optimal compromise solution and corresponding energy stream result。
2. model and method for solving according to claim l, is characterized in that, to obtain power transmission network, each actively distribution, the natural gas grid Basic Topological within dispatching cycle, branch impedance, fired power generating unit exert oneself the essential informations such as limit and data in step 1；And excavate and distributed constant matching based on to the wind speed of different time/region, intensity of illumination, electricity/gas/thermic load historical data, it is thus achieved that Random-fuzzy Time-space serial model and chance measure function representation scale wind-powered electricity generation, distributed power source, multiple-energy-source load etc.。
3. model according to claim l, it is characterized in that, with the power of common node set and voltage for shared variable in step 2, build and take into account economy under Static Security Constraints, low-carbon (LC), regenerative resource are dissolved, are dropped the multiobject SoS dynamic optimal energy flow models such as damage, wherein with power in common node set of TS and each ADN and voltage for shared variable, static constraint considers static security stability margin, ADN model considers the security constraint etc. of its Random-fuzzy multiple-energy-source equilibrium of supply and demand coupled at EH node with natural gas grid and natural gas grid。
4. model solution method according to claim 1, is characterized in that, converts bi-level optimal model and solves to couple coordination, at upper strata TS Optimized model, by shared variable z in step 3tIt is expressed as its local variable ηt, by z in lower floor's ADN Optimized modeltIt is expressed as local variable μt, define slack variable ct=ηt-μtAnd penalty function fπ(ct)=σ ct, wherein σ is along with iterations increases the dynamic penalty coefficient being gradually increased, by fπ(ct) be attached on each object function, make bi-level optimal model decoupling optimize and constrained each other, by iteration until ct< ε, it is thus achieved that composition decomposition optimization solution。
5. model solution method according to claim 1, it is characterized in that, in step 3, SoS hierarchy optimization is make ADN give full play to the self-centered coordination ability and realize dissolving of its internal distribution type renewable energy, first the method proposing to determine tri-kinds of operational modes of ADN, namely calculate the P under original stateLi,tIf, PLi,t< 0 illustrates that the internal distributed energy generating of ADN has more than needed, then first consider to call energy storage device charging storage, under the premise meeting security constraint, if still having more than needed after fully calling energy storage device, then to TS power transmission under the premise meeting safety and bus power constraints, now ADN is power supply status；If initial pLi,t> 0, illustrates that ADN can not supply by teaching display stand, then first consider to call energy storage device discharge supply, if still cannot be self-sufficient after fully calling, then absorb electrical power from TS, and ADN runs on load condition；If P can be made under original state or by calling energy storage deviceLi,t=0, then it is isolated island depending on ADN, and TS decoupling running optimizatin, in above process, to dissolve completely if regenerative resource cannot be realized under constraints, then the means reduction part being considered as active management is exerted oneself。
6. model solution method according to claim 1, it is characterized in that, the multiobjective optimization energy flow algorithm being based on approximate dynamic programming and NSGA-II adopted in step 3, NSGA-II is adopted to ask for Pareto optimal solution set, Pareto is solved to each non-domination solution concentrated, Fuzzy satisfaction computing formula less than normal is adopted to calculate the satisfaction of each of which desired value, calculate the comprehensive satisfaction of each non-domination solution again, selected the path of cumulative Maximum Satisfaction by the approximate dynamic programming of Policy iteration, constitute the solution of multiple target dynamic optimal energy stream。
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|---|---|---|---|---|
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| CN115454614A (en) * | 2022-11-14 | 2022-12-09 | 安徽大学 | Intelligent scheduling method for robot cluster energy supply |
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103887790A (en) * | 2014-03-05 | 2014-06-25 | 广州供电局有限公司 | Cooperative power flow control method and system for power transmission network and power distribution network |
| CN104866919A (en) * | 2015-05-20 | 2015-08-26 | 天津大学 | Multi-target planning method for power grid of wind farms based on improved NSGA-II |
-
2016
- 2016-02-04 CN CN201610081125.0A patent/CN105703369B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103887790A (en) * | 2014-03-05 | 2014-06-25 | 广州供电局有限公司 | Cooperative power flow control method and system for power transmission network and power distribution network |
| CN104866919A (en) * | 2015-05-20 | 2015-08-26 | 天津大学 | Multi-target planning method for power grid of wind farms based on improved NSGA-II |
Non-Patent Citations (1)
| Title |
|---|
| 罗斌: "基于NSGA-II的含风电场电力系统多目标调度计划研究", 《中国优秀硕士学位论文全文数据库 工程科技II辑》 * |
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