CN109325608A - Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness - Google Patents

Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness Download PDF

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CN109325608A
CN109325608A CN201810558427.1A CN201810558427A CN109325608A CN 109325608 A CN109325608 A CN 109325608A CN 201810558427 A CN201810558427 A CN 201810558427A CN 109325608 A CN109325608 A CN 109325608A
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generation resource
distributed generation
power
photovoltaic
formula
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CN109325608B (en
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王月强
张文博
李卫彬
张佳杰
仇张权
孙列
徐旭东
姚远
周睿
朱铮
周琤
张菲菲
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Shanghai Shine Energy Technology Co Ltd
State Grid Shanghai Electric Power Co Ltd
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State Grid Shanghai Electric Power Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • 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
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector
    • 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
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S10/00—Systems supporting electrical power generation, transmission or distribution
    • Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications

Abstract

The invention discloses a kind of consideration energy storage and the distributed generation resource Optimal Configuration Methods of meter and photovoltaic randomness, the following steps are included: first, based on Density Estimator and K-means clustering method, according to a large amount of photovoltaic power output historical data, the typical day scene generating method of distributed generation resource power output is constructed；Further, theoretical based on dual layer resist, on the basis of upper layer is using year the lowest cost as target, lower layer respectively from comprehensive two angles of community income and Utilities Electric Co.'s income, establish by planning and run camera and distributed generation resource Bi-level Programming Models；It is combined finally, generating random scene with distributed generation resource Bi-level Programming Models, the distributed generation resource configuration scheme for meeting workload demand is calculated by intelligent optimization algorithm.Bi-level Programming Models proposed by the present invention can provide the distributed generation resource configuration scheme including configuration node and capacity, system year totle drilling cost is effectively reduced from different interests side.

Description

Consider the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness
Technical field
The present invention is more particularly directed to a kind of consideration energy storage and the distributed generation resource Optimal Configuration Methods of meter and photovoltaic randomness.
Background technique
With the development of economy with the progress of generation technology, distributed power generation (distributed generation, DG) Start gradually access distribution on a large scale.Under the background that China advocates energy-saving and emission-reduction, it is since distributed power generation (DG) is most of The environmentally friendly energy, more and more researchs and investment start to put into the development of DG generation technology.According to country of China " the emerging energy industrial development planning " of Bureau of Energy's publication, arrives the year two thousand twenty, China's installed capacity of wind-driven power will be more than 1.5 hundred million kW, light Lying prostrate capacity of installed generator will be more than 20GW, and biomass power generation installed capacity will be more than 30GW.To properly settle extensive point The grid-connected and operation of cloth power supply is influenced to distribution network systems bring, and distributed generation technology specifically includes distributed generation resource Cutting-in control is distributed rationally with Optimized Operation as an important research contents in power industry industry.
Reasonable power configuration can reduce energy consumption, improve environmental pollution, the power quality and power supply reliability of power distribution network It can also be improved, under China's long range for a long time, large capacity transmission environment, DG can be China's traditional power grid It is very big to provide effectively supplement, development potentiality.In addition to this, after distributed generation resource access, with traditional power grid scheduling method phase Than, power scheduling work becomes increasingly complex in the fluctuation in the discreteness and power output in distribution because of distributed generation resource, because This is with greater need for considering the influence of large-scale distributed plant-grid connection to power distribution network traffic control.
For the randomness of distributed generation resource, the distributed generation resource based on Density Estimator and K-means cluster is constructed The typical day scene generating method of power output；The dual-layer optimization allocation models of polymorphic type distributed generation resource is established, and heredity is respectively adopted Algorithm and Particle Swarm Optimization power output upper and lower level optimization problem.Model can obtain taking into account community income and Utilities Electric Co.'s interests Polymorphic type distributed generation resource configuration scheme, is effectively reduced system operation cost.
Density Estimator: a large amount of historical datas are based on, data distribution are described by kernel function, the probability for obtaining variable is close Function is spent, to describe the randomness of variable.
Dual-layer optimization allocation models: emphasizing that, first with the logical relation for postponing operation, upper layer provides configuration scheme, under Layer carries out the operation of system according to the configuration scheme on upper layer and returns to upper layer, is suitable for two stages planning problem.
Distributed electrical Source Type is more, including wind-driven generator, photovoltaic cell, miniature gas turbine, diesel engine, energy storage Battery etc..Distributed generation resource access is conducive to improve distribution network reliability, reduces carbon emission, power grid construction is delayed to invest, but point Cloth power supply can also bring the uncertain problems such as voltage stability, distributed generation resource consumption to power distribution network.Therefore need to point Cloth power supply is made rational planning for, and to evade the problem of distributed generation resource is brought to power distribution network, and plays distribution to the maximum extent Formula power supply advantage.The configuration of distributed generation resource at present generally uses simple single level programming model, and contributes to wind-powered electricity generation, photovoltaic Uncertain shortcoming consider.
Summary of the invention
The object of the present invention is to provide a kind of consideration energy storage and the distributed generation resource sides of distributing rationally of meter and photovoltaic randomness The power output of uncertain power supply is described using Density Estimator scheme method, and is combined using that will plan with running Bi-level programming method is modeled, and realizes the distributed generation resource planning for considering randomness, main to plan that power supply includes photovoltaic cell Group, miniature gas turbine and energy-storage battery.
In order to achieve the goal above, the present invention is achieved by the following technical solutions:
A kind of distributed generation resource Optimal Configuration Method considering energy storage and meter and photovoltaic randomness, which is characterized in that include Following steps:
S1 carries out distributed generation resource power producing characteristics mathematical modeling, is distributed according to distributed generation resource principle and structure Formula power supply dual-layer optimization allocation models；
S2 determines the light of planning region using rational density estimation method based on planning region photovoltaic power output historical data Volt power output probability density function；The typical daylight volt power output sequence of planning region is generated using K-means clustering method；
S3, theoretical based on dual layer resist, using year the lowest cost as target, lower layer receives from comprehensive society respectively on upper layer Benefit and Utilities Electric Co.'s income angle are set out, and power distribution network distributed generation resource Bi-level Programming Models are established；
S4, is respectively adopted genetic algorithm and particle swarm algorithm joint solves the layer model up and down of bilayer model, acquires distribution Formula electricity optimization allocation plan and typical day distributed generation resource power curve.
The step S1 specifically:
S1.1 calculates photovoltaic cell group steady output characteristics:
In formula: PPV,NAnd ηN, η be respectively photovoltaic cell group rated power, specified intensity of illumination and practical intensity of illumination；
S1.2 calculates the steady output characteristics of miniature gas turbine:
PGT=q ηGTmgas
In formula: PGTIt is the power output of gas turbine；Q is the calorific value of fuel；ηGTIt is power generation gross efficiency； mgasIt is fuel Flow velocity rate；
S1.3 calculates the steady output characteristics of energy-storage battery:
In formula: n indicates moment number, WBATIndicate battery reserve of electricity, σiFor self discharge coefficient, ηBAT,CAnd ηBAT,DRespectively Charge and discharge efficiency, SBATIndicate charged ratio, WBAT,RIndicate the rated capacity of electric energy storage device,Indicate that n+1 moment storage is set Standby reserve of electricity, PBAT,CAnd PBAT,DRespectively charge and discharge power.
The step S2 specifically:
S2.1 generates the photovoltaic of each of one day 24 hour small period according to historical data using kernel density estimation method Power output probability density function:
In formula: K (u) is kernel function, and n is the number of days of historical data, and t indicates the t period, and h is the bandwidth of Density Estimator,It contributes for the photovoltaic of i-th day t period in historical data,It is close in the probability of the power output of t period for unit capacity photovoltaic Spend function；
K (u) may be expressed as:
K(||x-xc| |)=exp-| | x-xc||2/(2*σ2)}
In formula, xcFor the center of kernel function, σ is the width parameter of function, controls the radial effect range of function；
S2.2, the probability density function based on unit capacity photovoltaic power output generates photovoltaic power output probability and divides function, small to 24 When the photovoltaic power output probability distribution formula sequence of function carry out 10000 times using obtaining 10000 groups of photovoltaics power output day sequences, and adopt 3 typical daylight volt power output scenes are generated with K-means cluster.
The step S3 specifically:
Step S3.1, distributed generation resource dual-layer optimization allocation models upper layer plant-grid connection point and distribution in a distributed manner The configuration capacity of power supply be control variable, using year totle drilling cost as objective function, including distributed generation resource yearization invest at This and system annual operating and maintenance cost, objective function are expressed as follows:
In formula: f1For the year totle drilling cost of distribution after distributed generation resource access, CinsFor distributed generation resource yearization investment at This,For operating cost of the distribution at scene i, i.e. objective function of the lower layer at scene i, piFor typical day scene i appearance Probability, k is typical day scene quantity；R is discount rate, and y is duration of service, RiAnd ciRespectively indicate the configuration of equipment i Capacity and cost, ΩDGIndicate the distributed generation resource collection of configuration；
Constraint condition are as follows:
In formula:For the maximum configured capacity of distributed generation resource i, can be determined by distribution distributed power grid access restriction；For the maximum value of lower layer's objective function, the maximum value of lower layer's objective function without punishment can use, under which can reflect The convergence of layer optimization；
The scheduling strategy of step S3.2, distributed generation resource are sorted out, and under being established respectively for different scheduling strategies The typical day scene optimal operation model of layer.
The step S3.2 specifically:
It is to integrate the optimal dispatching of power netwoks strategy for principle of social benefit, the operation of object of planning distribution network system is total Expense is optimal to be used as optimization aim, objective function such as following formula:
In formula: Closs,tFor t hours grid loss expenses, Pj,tFor the tune for the distributed generation resource that number is j in t hours Degree power output, unit kW；COMjFor the operation and maintenance cost of the distributed generation resource of j, CGjFor the fuel cost of j distributed generation resource With CMPjFor the blowdown expense of j distributed generation resource.
The step S3.2 specifically:
Using Utilities Electric Co. benefit as the dispatching of power netwoks strategy of principle, by the power output and synthesis that adjust each unit The distributed generation resource and outside power purchase expense for considering Utilities Electric Co., enable Utilities Electric Co. to meet with the smallest cost local Workload demand, to obtain its objective function of maximum return such as following formula:
In formula: Pgrid,tFor power purchase power outside distribution, Cgrid,tThe purchase electricity price of external electrical network, Cprice,jNumber for j point The online stake electrovalence of cloth power supply.
It is described that integrate, social benefit is optimal to be for the dispatching of power netwoks strategy of principle and with Utilities Electric Co. benefit The constraint condition of the dispatching of power netwoks strategy of principle is identical, includes:
Power-balance constraint:
In formula: Pload,tAnd Qload,tFor the total active and load or burden without work of system；Qj,t、Qgrid,tAnd Qloss,tRespectively it is distributed Formula power supply, interconnection and distribution internal loss reactive power；
Interconnection capacity-constrained:
In formula: For the maximum allowable capacity of distribution outside coupling line.
Node voltage constraint:
Umin≤Ui,t≤Umax(i=1,2,3 ...)
In formula: Ui,tIndicate voltage of the node i in t moment, UminAnd UmaxRespectively indicate voltage management provide in distribution Net node voltage upper and lower limit；
Schedulable resource Climing constant:
Pi,t-Pi,t-1≤Ri,up
Pi,t-1-Pi,t≤Ri,down
Unit in model by Climing constant includes conventional power unit (with miniature gas unit；Ri,upAnd Ri,downTable respectively Show the Climing constant up and down of schedulable unit i, unit kW.
Energy storage charge and discharge balance limitation:
tdisc=tchar≤tmax
In formula: tmaxFor the maximum charge/discharge time of energy storage, tdisc、tcharIt must connect in each comfortable one day period It is continuous.
Described respectively optimizes the upper and lower layer of distributed generation resource Bi-level Programming Models using intelligent optimization algorithm, Upper layer mixed-integer nonlinear programming model is solved using genetic algorithm, lower layer's Non-Linear Programming is solved using particle swarm algorithm Model is described as follows:
S4.1 initializes upper layer chromosome population, total z chromosome；
Upper layer chromosome parameter is imported underlying model, for each chromosome, using Particle Swarm Optimization by S4.2 Method carries out running optimizatin to each typical day scene；
S4.3 returns the running optimizatin result under three kinds of all chromosomes after lower layer optimizes typical day scenes Upper layer is gone back to, each chromosome fitness in upper layer is calculated；
S4.4 is checked whether and is reached maximum number of iterations, if not up to maximum number of iterations, calculates according to fitness As a result upper layer chromosome is generated into population of new generation by selection, intersection and variation and returns to S4.2；Otherwise optimal dyeing is exported Body and optimization operating scheme；
S4.5, algorithm terminate.
Compared with prior art, the present invention having the advantage that
Bi-level Programming Models proposed by the present invention can be provided from different interests side including configuration node and capacity Distributed generation resource configuration scheme, system year totle drilling cost is effectively reduced.
The random power output that uncertain power supply is considered in the planning stage is conducive to the robustness for enhancing prioritization scheme.
Mentality of designing of the present invention is clear, and usage mode is relatively simple, engineering in practice, have wide applicability.
For the randomness of distributed generation resource, the distributed generation resource based on Density Estimator and K-means cluster is constructed The typical day scene generating method of power output；The dual-layer optimization allocation models of polymorphic type distributed generation resource is established, and heredity is respectively adopted Algorithm and Particle Swarm Optimization power output upper and lower level optimization problem.Model can obtain taking into account community income and Utilities Electric Co.'s interests Polymorphic type distributed generation resource configuration scheme, is effectively reduced system operation cost.
Detailed description of the invention
Fig. 1 is the flow chart of step S4 of the present invention.
Specific embodiment
The present invention is further elaborated by the way that a preferable specific embodiment is described in detail below in conjunction with attached drawing.
A kind of distributed generation resource Optimal Configuration Method considering energy storage and meter and photovoltaic randomness, which is characterized in that include Following steps:
S1 carries out distributed generation resource power producing characteristics mathematical modeling, is distributed according to distributed generation resource principle and structure Formula power supply dual-layer optimization allocation models；
S2 determines the light of planning region using rational density estimation method based on planning region photovoltaic power output historical data Volt power output probability density function；The typical daylight volt power output sequence of planning region is generated using K-means clustering method；
S3, theoretical based on dual layer resist, using year the lowest cost as target, lower layer receives from comprehensive society respectively on upper layer Benefit and Utilities Electric Co.'s income angle are set out, and power distribution network distributed generation resource Bi-level Programming Models are established；
S4, is respectively adopted genetic algorithm and particle swarm algorithm joint solves the layer model up and down of bilayer model, acquires distribution Formula electricity optimization allocation plan and typical day distributed generation resource power curve.
The step S1 specifically:
S1.1 calculates photovoltaic cell group steady output characteristics:
In formula: PPV,NAnd ηN, η be respectively photovoltaic cell group rated power, specified intensity of illumination and practical light；Its power because Number is adjustable before lag 0.98~advanced 0.98.
S1.2 calculates the steady output characteristics of miniature gas turbine:
PGT=q ηGTmgas
In formula: PGTIt is the power output of gas turbine；Q is the calorific value of fuel；ηGTIt is power generation gross efficiency； mgasIt is fuel Flow velocity rate；
S1.3 calculates the steady output characteristics of energy-storage battery:
In formula: n indicates moment number, WBATIndicate battery reserve of electricity, σiFor self discharge coefficient, ηBAT,CAnd ηBAT,DRespectively Charge and discharge efficiency, SBATIndicate charged ratio, WBAT,RIndicate the rated capacity of electric energy storage device, PBAT,CAnd PBAT,DIt respectively fills, put Electrical power.Energy-storage battery, which can be regarded as P, Q when distributing rationally, can decouple, the backup power source with Independent adjustable performance or cut Peak load equipment uses.Its output power is held essentially constant in charge and discharge process.
The step S2 specifically:
S2.1 generates the photovoltaic of each of one day 24 hour small period according to historical data using kernel density estimation method Power output probability density function:
In formula: K (u) is kernel function, and n is the number of days of historical data, and t indicates the t period, and h is the bandwidth of Density Estimator,It contributes for the photovoltaic of i-th day t period in historical data,It is close in the probability of the power output of t period for unit capacity photovoltaic Spend function；
K (u) may be expressed as:
K(||x-xc| |)=exp-| | x-xc||2/(2*σ2)}
In formula, xcFor the center of kernel function, σ is the width parameter of function, controls the radial effect range of function；
S2.2, the probability density function based on unit capacity photovoltaic power output generates photovoltaic power output probability and divides function, small to 24 When the photovoltaic power output probability distribution formula sequence of function carry out 10000 times using obtaining 10000 groups of photovoltaics power output day sequences, and adopt 3 typical daylight volt power output scenes are generated with K-means cluster.
The step S3 specifically:
Step S3.1, distributed generation resource dual-layer optimization allocation models upper layer power supply (Distributed in a distributed manner Generator, DG) configuration capacity of access point and DG is control variable, using year totle drilling cost as objective function, wherein wrapping It includes DGization cost of investment and system annual operating and maintenance cost, objective function is expressed as follows:
In formula: f1For the year totle drilling cost of distribution after distributed generation resource access, CinsFor distributed generation resource yearization investment at This,For operating cost of the distribution at scene i, i.e. objective function of the lower layer at scene i, piFor typical day scene i appearance Probability, k is typical day scene quantity；R is discount rate, and y is duration of service, RiAnd ciRespectively indicate the configuration of equipment i Capacity and cost, ΩDGIndicate the DG collection of configuration；
Constraint condition are as follows:
In formula:It, can be true by distribution distributed power grid access restriction for the maximum configured capacity of distributed generation resource i It is fixed；For the maximum value of lower layer's objective function, the maximum value of lower layer's objective function without punishment can use, which can be anti- Reflect the convergence of lower layer's optimization；
The scheduling strategy of step S3.2, distributed generation resource are sorted out, and under being established respectively for different scheduling strategies The typical day scene optimal operation model of layer specifically according to the present Research of power distribution network traffic control, is determining Optimized Operation When the target of model, it may stand in different interests sides and consider a problem.Therefore, the present invention is by the scheduling strategy of distributed generation resource Two major classes are summarized as, and establish the typical day scene optimal operation model of lower layer respectively for two kinds of scheduling strategies.
The step S3.2 specifically:
To integrate the optimal dispatching of power netwoks strategy for principle of social benefit, power distribution network is with high efficiency, low energy consumption, low pollution Principle carry out system optimization scheduling, by the operation total cost of object of planning distribution network system it is optimal be used as optimization aim, specifically Operation expense, fuel cost and blowdown cost including generator unit in wear and tear expense, external purchases strategies, system, Its objective function such as following formula:
In formula: Closs,tFor t hours grid loss expenses, Pj,tFor the tune for the distributed generation resource that number is j in t hours Degree power output, unit kW；COMjFor the operation and maintenance cost of the distributed generation resource of j, CGjFor the fuel cost of j distributed generation resource With CMPjFor the blowdown expense of j distributed generation resource, using member/kWh as unit.
The step S3.2 specifically:
Using Utilities Electric Co. benefit as the dispatching of power netwoks strategy of principle, by the power output and synthesis that adjust each unit The distributed generation resource and outside power purchase expense for considering Utilities Electric Co., enable Utilities Electric Co. to meet with the smallest cost local Workload demand, to obtain its objective function of maximum return such as following formula:
In formula: Pgrid,tFor power purchase power outside distribution, Cgrid,tThe purchase electricity price of external electrical network, Cprice,jNumber for j point The online stake electrovalence of cloth power supply, using member/kWh as unit.
It is described that integrate, social benefit is optimal to be for the dispatching of power netwoks strategy of principle and with Utilities Electric Co. benefit The constraint condition of the dispatching of power netwoks strategy of principle is identical, includes:
Power-balance constraint:
In formula: Pload,tAnd Qload,tFor the total active and load or burden without work of system；Qj,t、Qgrid,tAnd Qloss,tRespectively DG, Interconnection and distribution internal loss reactive power；
Interconnection capacity-constrained:
In formula: For the maximum allowable capacity of distribution outside coupling line.
Node voltage constraint:
Umin≤Ui,t≤Umax(i=1,2,3 ...)
In formula: Ui,tIndicate voltage of the node i in t moment, UminAnd UmaxRespectively indicate voltage management provide in distribution Net node voltage upper and lower limit；
Schedulable resource Climing constant:
Pi,t-Pi,t-1≤Ri,up(increasing power output)
Pi,t-1-Pi,t≤Ri,down(subtracting power output)
Unit in model by Climing constant includes conventional power unit (the outer power purchase of system) and miniature gas unit；Ri,up And Ri,downRespectively indicate the Climing constant up and down of schedulable unit i, unit kW.
Energy storage charge and discharge balance limitation:
tdisc=tchar≤tmax
In formula: tmaxFor the maximum charge/discharge time of energy storage, tdisc、tcharIt must connect in each comfortable one day period It is continuous, that is, the continuous charge and discharge of battery are required to keep regulation convenient and avoid energy storage device aging caused by excessive charge and discharge.
Referring to Fig. 1, it is described using intelligent optimization algorithm respectively to the upper and lower layer of distributed generation resource Bi-level Programming Models It optimizes, upper layer mixed-integer nonlinear programming model is solved using genetic algorithm, it is non-to solve lower layer using particle swarm algorithm Linear programming model is described as follows:
S4.1 initializes upper layer chromosome population, total z chromosome；
Upper layer chromosome parameter is imported underlying model, for each chromosome, using Particle Swarm Optimization by S4.2 Method carries out running optimizatin to each typical day scene；
S4.3 returns the running optimizatin result under three kinds of all chromosomes after lower layer optimizes typical day scenes Upper layer is gone back to, each chromosome fitness in upper layer is calculated；
S4.4 is checked whether and is reached maximum number of iterations, if not up to maximum number of iterations, calculates according to fitness As a result upper layer chromosome is generated into population of new generation by selection, intersection and variation and returns to S4.2；Otherwise optimal dyeing is exported Body and optimization operating scheme；
S4.5, algorithm terminate.
In conclusion a kind of distributed generation resource Optimal Configuration Method for considering energy storage and meter and photovoltaic randomness of the present invention, The power output of uncertain power supply is described using Density Estimator scheme, and use will plan pair combined with operation Layer planing method is modeled, and realizes the distributed generation resource planning for considering randomness, main to plan that power supply includes photovoltaic cell Group, miniature gas turbine and energy-storage battery.
It is discussed in detail although the contents of the present invention have passed through above preferred embodiment, but it should be appreciated that above-mentioned Description be not considered as limitation of the present invention.After those skilled in the art have read above content, for the present invention A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited by the attached claims It is fixed.

Claims (8)

1. a kind of distributed generation resource Optimal Configuration Method for considering energy storage and meter and photovoltaic randomness, which is characterized in that comprising such as Lower step:
S1 carries out distributed generation resource power producing characteristics mathematical modeling, obtains distributed generation resource according to distributed generation resource principle and structure Dual-layer optimization allocation models；
S2 determines that the photovoltaic of planning region goes out using rational density estimation method based on planning region photovoltaic power output historical data Power probability density function；The typical daylight volt power output sequence of planning region is generated using K-means clustering method；
S3, theoretical based on dual layer resist, upper layer is using year the lowest cost as target, and lower layer is respectively from comprehensive community income and electricity Power corporate income angle is set out, and power distribution network distributed generation resource Bi-level Programming Models are established；
S4, is respectively adopted genetic algorithm and particle swarm algorithm joint solves the layer model up and down of bilayer model, acquires distributed electrical Source optimization allocation plan and typical day distributed generation resource power curve.
2. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as described in claim 1, it is special Sign is, the step S1 specifically:
S1.1 calculates photovoltaic cell group steady output characteristics:
In formula: PPV,NAnd ηN, η be respectively photovoltaic cell group rated power, specified intensity of illumination and practical intensity of illumination；
S1.2 calculates the steady output characteristics of miniature gas turbine:
PGT=q ηGTmgas
In formula: PGTIt is the power output of gas turbine；Q is the calorific value of fuel；ηGTIt is power generation gross efficiency；mgasIt is the flow velocity of fuel Rate；
S1.3 calculates the steady output characteristics of energy-storage battery:
In formula: n indicates moment number, WBATIndicate battery reserve of electricity, σiFor self discharge coefficient, ηBAT,CAnd ηBAT,DIt respectively fills, put Electrical efficiency, SBATIndicate charged ratio, WBAT,RIndicate the rated capacity of electric energy storage device,Indicate n+1 moment electric energy storage device storage Amount, PBAT,CAnd PBAT,DRespectively charge and discharge power.
3. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as described in claim 1, it is special Sign is, the step S2 specifically:
S2.1 is contributed generally according to historical data using the photovoltaic that kernel density estimation method generates each of one day 24 hour small period Rate density function:
In formula: K (u) is kernel function, and n is the number of days of historical data, and t indicates the t period, and h is the bandwidth of Density Estimator,To go through The photovoltaic power output of i-th day t period in history data,For unit capacity photovoltaic the power output of t period probability density function；
K (u) may be expressed as:
K(||x-xc| |)=exp-| | x-xc||2/(2*σ2)}
In formula, xcFor the center of kernel function, σ is the width parameter of function, controls the radial effect range of function；
S2.2, the probability density function based on unit capacity photovoltaic power output generated photovoltaic power output probability and divides function, to 24 hours Photovoltaic, which contributes the progress of the probability distribution formula sequence of function 10000 times to use, obtains 10000 groups of photovoltaics power output day sequences, and uses K- Means cluster generates 3 typical daylight volt power output scenes.
4. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as described in claim 1, it is special Sign is, the step S3 specifically:
Step S3.1, distributed generation resource dual-layer optimization allocation models upper layer plant-grid connection point and distributed generation resource in a distributed manner Configuration capacity is control variable, using year totle drilling cost as objective function, including distributed generation resource year cost of investment and System annual operating and maintenance cost, objective function are expressed as follows:
In formula: f1For the year totle drilling cost of distribution after distributed generation resource access, CinsFor distributed generation resource year cost of investment,For Operating cost of the distribution at scene i, i.e. objective function of the lower layer at scene i, piFor the probability that typical day scene i occurs, k For typical day scene quantity；R is discount rate, and y is duration of service, RiAnd ciRespectively indicate equipment i configuration capacity and at This, ΩDGIndicate the distributed generation resource collection of configuration；
Constraint condition are as follows:
In formula:For the maximum configured capacity of distributed generation resource i, can be determined by distribution distributed power grid access restriction； For the maximum value of lower layer's objective function, the maximum value of lower layer's objective function without punishment can use, which can reflect that lower layer is excellent The convergence of change；
The scheduling strategy of step S3.2, distributed generation resource are sorted out, and establish lower layer's allusion quotation respectively for different scheduling strategies Type day scene optimal operation model.
5. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as claimed in claim 4, it is special Sign is, the step S3.2 specifically:
To integrate the optimal dispatching of power netwoks strategy for principle of social benefit, most by the operation total cost of object of planning distribution network system It is excellent to be used as optimization aim, objective function such as following formula:
In formula: Closs,tFor t hours grid loss expenses, Pj,tScheduling for the distributed generation resource that number is j in t hours goes out Power, unit kW；COMjFor the operation and maintenance cost of the distributed generation resource of j, CGjFor the fuel cost of j distributed generation resource, CMPj For the blowdown expense of j distributed generation resource.
6. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as claimed in claim 4, it is special Sign is, the step S3.2 specifically:
Using Utilities Electric Co. benefit as the dispatching of power netwoks strategy of principle, by adjusting the power output of each unit and comprehensively considering electricity The distributed generation resource of power company and external power purchase expense, enable Utilities Electric Co. to meet local load need with the smallest cost It asks, to obtain its objective function of maximum return such as following formula:
In formula: Pgrid,tFor power purchase power outside distribution, Cgrid,tThe purchase electricity price of external electrical network, Cprice,jNumber be j distributed electrical The online stake electrovalence in source.
7. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as claimed in claim 4, it is special Sign is, described it is optimal to integrate social benefit for the dispatching of power netwoks strategy of principle and with Utilities Electric Co. benefit is former The constraint condition of dispatching of power netwoks strategy then is identical, includes:
Power-balance constraint:
In formula: Pload,tAnd Qload,tFor the total active and load or burden without work of system；Qj,t、Qgrid,tAnd Qloss,tRespectively distributed electrical Source, interconnection and distribution internal loss reactive power；
Interconnection capacity-constrained:
In formula: The maximum allowable capacity of distribution outside coupling line.
Node voltage constraint:
Umin≤Ui,t≤Umax(i=1,2,3 ...)
In formula: Ui,tIndicate voltage of the node i in t moment, UminAnd UmaxRespectively indicate voltage management provide in power distribution network node Voltage upper and lower limit；
Schedulable resource Climing constant:
Pi,t-Pi,t-1≤Ri,up
Pi,t-1-Pi,t≤Ri,down
Unit in model by Climing constant includes conventional power unit (with miniature gas unit；Ri,upAnd Ri,downRespectively indicating can Dispatch the Climing constant up and down of unit i, unit kW.
Energy storage charge and discharge balance limitation:
tdisc=tchar≤tmax
In formula: tmaxFor the maximum charge/discharge time of energy storage, tdisc、tcharIt must be continuous in each comfortable one day period.
8. the distributed generation resource Optimal Configuration Method of energy storage and meter and photovoltaic randomness is considered as described in claim 1, it is special Sign is, described to be optimized respectively to the upper and lower layer of distributed generation resource Bi-level Programming Models using intelligent optimization algorithm, adopts Upper layer mixed-integer nonlinear programming model is solved with genetic algorithm, lower layer's Non-Linear Programming mould is solved using particle swarm algorithm Type is described as follows:
S4.1 initializes upper layer chromosome population, total z chromosome；
Upper layer chromosome parameter is imported underlying model, for each chromosome, using particle swarm optimization algorithm to every by S4.2 A typical case's day scene carries out running optimizatin；
S4.3 returns to the running optimizatin result under three kinds of all chromosomes after lower layer optimizes typical day scenes Layer calculates each chromosome fitness in upper layer；
S4.4 is checked whether and is reached maximum number of iterations, will according to fitness calculated result if not up to maximum number of iterations Upper layer chromosome generates population of new generation by selection, intersection and variation and returns to S4.2；Otherwise optimal chromosome and excellent is exported Change operating scheme；
S4.5, algorithm terminate.
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