CN109508499A - Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method - Google Patents
Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method Download PDFInfo
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Abstract
The multi-period more optimal on-positions of scene distribution formula power supply of one kind and capacity research method, comprising: establish component models, including distributed photovoltaic model, distributed blower model and load model；Objective function is established, is the power purchase expense of the investment, O&M expense and residual value and power distribution network superior power grid with the minimum objective function of comprehensive cost final value in project period, including distributed blower and photovoltaic；Constraint condition is established, constraint condition includes: the trend constraint of distribution network, distributed generation resource invests to build sequence constraint, position voltage constrains and branch current constraint；Objective function is optimized using particle swarm algorithm in constraint condition, including particle swarm algorithm coding, for the processing of constraint condition and the solution based on particle swarm algorithm and OpenDSS.The present invention considers that the DG addressing constant volume scheme of multi-period more scenes can significantly reduce integrated operation expense, promotes the economy of power distribution network after distributed generation resource access.
Description
Technical field
The present invention relates to a kind of optimal on-positions of distributed generation resource and capacity research method.More particularly to it is a kind of more when
The more optimal on-positions of scene distribution formula power supply of section and capacity research method.
Background technique
For a long time, the irrationality of energy resource structure and efficiency of energy utilization continue it is relatively low bring many environment and
Social concern.With the relieving of Power policy, distributed generation resource (Distributed Generation, DG) is used as a kind of green
Efficient power generation mode shows extensive development trend: on the one hand, the development of regional complex energy resource system pushes more points
The access of cloth power supply, on the other hand, the especially preferential grid-connected policy of China's " photovoltaic poverty alleviation " policy and photovoltaic in recent years
Under excitation, a large amount of distributed generation resource access power distribution networks certainly will be had.The access of distributed generation resource is promoting renewable energy utilization
Voltage, network loss, power etc. on power distribution network while Optimization of Energy Structure, is also generated a series of influences by rate.In addition, with
Essential change is occurring for the access of distributed generation resource, energy storage device and polynary load, the form of power distribution network, and traditional is single
When discontinuity surface, single scene distribution system analysis technology be difficult to meet at present to power supply randomness, multi-operating condition, multimode
The analysis requirement of state, needs by suitable for time stimulatiom that is multi-period, being capable of simulation distribution formula power supply and energy storage device model
The impact analysis of tool progress distributed generation resource access.Therefore, influence of the DG to power distribution network how is accurately analyzed, and realizes that DG connects
Enter making rational planning for for position and capacity, it has also become urgent problem to be solved.
Currently, domestic and foreign scholars to the optimal access problem of DG carried out a series of researchs and achieve it is related at
Fruit, but the determination of programme is to be known as background with target year load, does not consider practical distribution network load sustainable growth
In the case of DG multistage dynamic programming problems, programme is not careful specific enough.On the other hand, more scenes are multistage after DG access
The timing Load flow calculation of section is also required to the technical support of corresponding emulation platform.
Summary of the invention
The technical problem to be solved by the invention is to provide a kind of multi-period more optimal on-positions of scene distribution formula power supply
With capacity research method.
The technical scheme adopted by the invention is that: a kind of optimal on-position of multi-period more scene distribution formula power supplys and capacity
Research method includes the following steps:
1) component models, including distributed photovoltaic model, distributed blower model and load model are established；
2) objective function is established, is with the minimum objective function of comprehensive cost final value in project period, including distributed wind
Investment, O&M expense and the residual value of machine and photovoltaic and the power purchase expense of power distribution network superior power grid；
3) constraint condition is established, constraint condition includes: that the trend constraint of distribution network, distributed generation resource invest to build sequence about
Beam, position voltage constraint and branch current constraint；
4) objective function is optimized using particle swarm algorithm in constraint condition, including particle swarm algorithm coding,
Processing for constraint condition and the solution based on particle swarm algorithm and OpenDSS.
Described in step 1),
(1.1) distributed photovoltaic model: the power output of photovoltaic depends primarily on intensity of illumination, before more scene timing simulations
It putting, photovoltaic power output and the relationship of intensity of illumination are distributed photovoltaic model, it is expressed as follows:
In formula: PbIt is the real-time power output of photovoltaic；PsnIndicate the rated power of photovoltaic；GstdIndicate specified intensity of illumination；RcTable
Show the light intensity of any certain strength, i.e., the relationship of photovoltaic power output and light intensity is by non-linear to linear turning point；GbτIndicate τ
The real-time light intensity of hour；
(1.2) distributed blower model, as the power output P of wind-driven generatorwindWith the following institute of the functional relation of wind speed v
Show:
Wherein, PwindmaxFor the rated power of blower；vnFor the incision wind speed of blower；vrFor the rated wind speed of blower；voFor
The cut-out wind speed of blower；
(1.3) load model is expressed as follows:
Lt=Lp×Ps(t) (3)
In formula, LtFor any hour workload demand amount, LpFor year load peak；PsIt (t) is each hour under s-th of scene
Load and year load peak proportionality coefficient.
Objective function described in step 2) are as follows:
MinC=CPV+CWTG+COP (4)
In formula, CPVIndicate the overall life cycle cost final value of distributed photovoltaic, CWTGIndicate week life-cycle of distributed blower
Period cost final value, COPIndicate the power purchase expense final value of power distribution network superior power grid in project period；CPVWith CWTGCalculation formula such as
Under:
In formula, T is planning stage sum；NPVWith NWTGRespectively indicate the node total number yet to be built of photovoltaic and blower in power distribution network；
RPVWith RWTGRespectively indicate single group photovoltaic or blower invests to build cost；βi,tWith γi,tThe t stage of planning is respectively indicated in node i
The group number of photovoltaic and blower；R is social discount rate；M and n respectively indicates the service life of photovoltaic and blower；upvWith uwtgRespectively indicate light
The maintenance cost ratio of volt and blower；zpvWith zwtgRespectively indicate the residual value cost ratio of photovoltaic and blower；ItIt is converted for final value and is
Number, calculation method are as follows:
In formula, ntFor t-th of stage year, atFor the initial time in t-th of stage, aTThe time is terminated for project period；
The power purchase expense final value of power distribution network superior power grid is expressed as follows in project period:
In formula, SendFor year scene sum, including four typical scenes of spring, summer, autumn and winter；τendIndicate the hour under any scene
Number；CPIndicate electricity price；WtsτIndicate s-th of scene of t stage under τ moment power distribution network superior power grid purchase of electricity, by load,
Distributed generation resource generated energy and route network loss three parts composition, calculation formula are as follows:
In formula, htIndicate load growth rate of the t stage compared with planning the starting year；Ls,τ,kIndicate the τ moment the under s scene
The initial load of k load point；K indicates load point set；Pbτ moment single group photovoltaic goes out in real time under (s, τ) expression s scene
Power is calculated according to distributed photovoltaic model；Pwind(s, τ) indicate s scene under τ moment single group blower real-time power output, according to point
Cloth blower model calculates；JiIndicate distribution network line set；Pj,s,τWith Qj,s,τThe τ moment, it is first to flow through route j respectively under s scene
The active and reactive power at end；Uj,s,τFor route j first section voltage；RjIndicate the resistance of route j.
Described in step 3):
(3.1) trend constraint of distribution network
In formula, Pi、QiActive and reactive injecting power respectively at node i；Ui、UjRespectively node i, j voltage magnitude；
Gij、BijThe respectively conductance of branch ij, susceptance；θijThe phase difference of voltage between node i, j；
(3.2) distributed generation resource invests to build sequence constraint
In formula, βi,tWith γi,tRespectively indicate group number of the t stage in node i photovoltaic and blower of planning；βi,t+1With
γi,t+1Respectively indicate group number of the t stage in node i photovoltaic and blower of planning；
During the constraint condition indicates the multi-period planning of distributed generation resource, the photovoltaic or blower of next stage node i
Group number cannot be less than on last stage, i.e., any node distributed generation resource cannot remove after investing to build；
(3.3) node voltage constrains
Uimin< Ui< Uimax (12)
In formula, Uimin、UimaxThe respectively lower and upper limit of node i voltage value；
The constraint condition indicates that during carrying out more scene timing Power flow simulations with OpenDSS, any moment is each
The voltage magnitude of node must be between the safe bound of permission；
(3.4) branch current constrains
In formula, IkIndicate actual branch current,Indicate the upper limit of branch current value；
The constraint condition indicates that the branch current at any moment is no more than the electricity that the branch allows during Power flow simulation
Flow maximum value.
Particle swarm algorithm described in step 4) encodes
Segment encoding mode is used to optimization object, coded format D of each particle in search space is indicated are as follows:
In formula, the preceding N of DPVA variableIndicate number of the first stage photovoltaic of planning under each node to be selected；Indicate number of the planning first stage distribution blower under each node to be selected；Indicate planning
Number of the second stage photovoltaic of phase under each node to be selected；Indicate second stage blower each to be selected
Number under node, and so on until reach planning stage sum T；Particularly, since particle is in the location variable of each dimension
diWith eiIt is necessary for integer, therefore after particle updates oneself position with reference to itself locally optimal solution and globally optimal solution, it need to be into
Row is rounded downwards, to meet the discretization requirement of distributed generation resource installation number；
For the processing of constraint condition described in step 4), if being to be unsatisfactory for distributed generation resource throwing after updating particle position
Sequence constraint is built, i.e., the distributed generation resource number of same node the latter half is less than previous stage, then forces the Node distribution
Formula power supply number is set as identical as previous stage；If node voltage constraint and branch current constraint are unsatisfactory for, in target letter
Penalty term is supplemented in number, respectively such as penalty term h given below1(t)、h2(t), if meeting node voltage constraint and branch current
Constraint, by penalty term zero setting,
In formula, MUWith MIRespectively indicate the penalty coefficient of voltage out-of-limit and electric current more in limited time；Uimin、UimaxRespectively node i
The lower and upper limit of voltage value；UiFor the virtual voltage amplitude of node i；IkIndicate actual branch current,Indicate branch electricity
The upper limit of flow valuve.
The solution based on particle swarm algorithm and OpenDSS, comprising:
(4.1) distribution network parameters and particle swarm algorithm parameter are inputted, wire topologies and resistance, distribution are specifically included
Power supply node to be selected, the aceleration pulse in particle swarm algorithm, inertial factor, constraint factor, while inputting distributed photovoltaic, wind
The parameter of machine, load cell obtains the distributed generation resource power output sequence and load value of each typical case's day under the more scenes of different phase；
(4.2) population is initialized, determines initial multi-period distributed generation resource access scheme；
(4.3) OpenDSS emulation platform is combined to carry out timing tide to the corresponding distributed generation resource programme of each particle
Stream calculation, obtain more scenes it is multi-period under voltage, network loss, power distribution information, calculate each node voltage and branch current；
(4.4) judge whether to meet node voltage constraint and branch current constraint, if being unsatisfactory for node voltage constraint and branch
Road restriction of current, then supplementary type (15) and penalty term shown in (16) in objective function, constrain and prop up if meeting node voltage
Road restriction of current, by penalty term zero setting；
(4.5) combined objective function target function value corresponding with penalty term each particle of calculating, as particle fitness, obtain
To the optimal value of each particle itself and the global optimum of population；
(4.6) judge whether particle swarm algorithm meets termination condition, i.e., whether global optimum restrains or reach maximum and change
Generation number is to export optimal solution and decode to obtain multistage distributed generation resource programme, otherwise enters in next step；
(4.7) carry out population and update operation, obtain new population Position And Velocity, when update adjustment particle coding with
The discretization for meeting distributed generation resource number requires and invests to build the constraint of sequence, and returns to (4.3) step.
Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method of the invention, use OpenDSS
The timing Power flow simulation under more scenes is carried out, the influence being distributed after DG access to distribution power flow can be accurately analyzed；With the multistage
The minimum target of final value expense of project period considers the construction of the trend constraints and DG Dynamic Programming such as node voltage, branch current
Sequence constraint proposes the optimal access model of DG under multi-period more scenes；Model is optimized in conjunction with particle swarm algorithm and is asked
Solution realizes seeking for optimal access scheme by calling repeatedly for MATLAB and OpenDSS.
DG different position and capacity under the conditions of the present invention uses OpenDSS emulation platform that can accurately analyze more scene timing
Influence to distribution network voltage and network loss；In conjunction with optimal access model, can seek more in load dynamic propagation process project period
Stage optimum programming scheme considers the DG addressing constant volume of multi-period more scenes compared with traditional target year integrated planning scheme
Scheme can significantly reduce integrated operation expense, promote the economy of power distribution network after distributed generation resource access.
Detailed description of the invention
Fig. 1 is intensity of illumination temporal characteristics curve；
Fig. 2 is wind speed temporal characteristics curve；
Fig. 3 is load temporal characteristics curve；
Fig. 4 is IEEE33 node system；
System node voltage timing diagram when Fig. 5 is the access of no photovoltaic；
Fig. 6 is the node voltage timing diagram of 18 nodes just more in limited time；
Fig. 7 is total network loss with distributed generation resource volume change figure.
Specific embodiment
To multi-period more optimal on-positions of scene distribution formula power supply of the invention and hold below with reference to embodiment and attached drawing
Quantity research method is described in detail.
Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method of the invention, in conjunction with OpenDSS
Emulation platform carries out more scene timing Load flow calculations after distributed blower and photovoltaic access；And propose a year comprehensive cost minimum
DG multistage dynamic programming model for target and the model solution algorithm based on particle swarm algorithm；DG is planned using MATLAB
Model programming realizes that being nested with OpenDSS realizes the determination of the multistage optimal access scheme of more scenes；Finally, choosing allusion quotation
Type example carries out the planning of DG under the multistage, it was demonstrated that the practicability and validity of mentioned model and method of the invention.
Multi-period more optimal on-positions of scene distribution formula power supply and capacity research method of the invention, including walk as follows
It is rapid:
1) component models, including distributed photovoltaic model, distributed blower model and load model are established；It is wherein described
:
(1.1) distributed photovoltaic model: the power output of photovoltaic depends primarily on intensity of illumination, before more scene timing simulations
It putting, photovoltaic power output and the relationship of intensity of illumination are distributed photovoltaic model, it is expressed as follows:
In formula: PbIt is the real-time power output of photovoltaic；PsnIndicate the rated power of photovoltaic；GstdIndicate specified intensity of illumination；RcTable
Show the light intensity of any certain strength, i.e., the relationship of photovoltaic power output and light intensity is by non-linear to linear turning point；GbτIndicate τ
The real-time light intensity of hour；Real-time light intensity is divided into four typical scenes of spring, summer, autumn and winter, and the illumination of different scenes next typical day is strong
It is as shown in Figure 1 to spend characteristic curve.
(1.2) distributed blower model, as the power output P of wind-driven generatorwindWith the following institute of the functional relation of wind speed v
Show:
Wherein, PwindmaxFor the rated power of blower；vnFor the incision wind speed of blower；vrFor the rated wind speed of blower；voFor
The cut-out wind speed of blower；Wind speed is equally divided into four scenes of spring, summer, autumn and winter, and the wind speed change curve under each scene typical day is such as
Shown in Fig. 2.
(1.3) load also uses four typical scenes of spring, summer, autumn and winter, the timing of each typical day, load model under different scenes
It is expressed as follows:
Lt=Lp×Ps(t) (3)
In formula, LtFor any hour workload demand amount, LpFor year load peak；PsIt (t) is each hour under s-th of scene
Load and year load peak proportionality coefficient, situation of change is as shown in Figure 3.
2) objective function is established, is with the minimum objective function of comprehensive cost final value in project period, including distributed wind
Investment, O&M expense and the residual value of machine and photovoltaic and the power purchase expense of power distribution network superior power grid；Described in objective function
Are as follows:
MinC=CPV+CWTG+COP (4)
In formula, CPVIndicate the overall life cycle cost final value of distributed photovoltaic, CWTGIndicate week life-cycle of distributed blower
Period cost final value, COPIndicate the power purchase expense final value of power distribution network superior power grid in project period；CPVWith CWTGCalculation formula such as
Under:
In formula, T is planning stage sum；NPVWith NWTGRespectively indicate the node total number yet to be built of photovoltaic and blower in power distribution network；
RPVWith RWTGRespectively indicate single group photovoltaic or blower invests to build cost；βi,tWith γi,tThe t stage of planning is respectively indicated in node i
The group number of photovoltaic and blower；R is social discount rate；M and n respectively indicates the service life of photovoltaic and blower；upvWith uwtgRespectively indicate light
The maintenance cost ratio of volt and blower；zpvWith zwtgRespectively indicate the residual value cost ratio of photovoltaic and blower；ItIt is converted for final value and is
Number, calculation method are as follows:
In formula, ntFor t-th of stage year, atFor the initial time in t-th of stage, aTThe time is terminated for project period；
The power purchase expense final value of power distribution network superior power grid is expressed as follows in project period:
In formula, SendFor year scene sum, including four typical scenes of spring, summer, autumn and winter；τendIndicate the hour under any scene
Number；CPIndicate electricity price；WtsτIndicate s-th of scene of t stage under τ moment power distribution network superior power grid purchase of electricity, by load,
Distributed generation resource generated energy and route network loss three parts composition, calculation formula are as follows:
In formula, htIndicate load growth rate of the t stage compared with planning the starting year；Ls,τ,kIndicate the τ moment the under s scene
The initial load of k load point；K indicates load point set；Pbτ moment single group photovoltaic goes out in real time under (s, τ) expression s scene
Power is calculated according to distributed photovoltaic model；Pwind(s, τ) indicate s scene under τ moment single group blower real-time power output, according to point
Cloth blower model calculates；JiIndicate distribution network line set；Pj,s,τWith Qj,s,τThe τ moment, it is first to flow through route j respectively under s scene
The active and reactive power at end；Uj,s,τFor route j first section voltage；RjIndicate the resistance of route j.
3) constraint condition is established, the optimal access of Distributed Generation in Distribution System needs to guarantee the safety and stability fortune of power grid
Row, while in view of the engineering schedule of distributed generation resource in multi-period planning process, constraint condition includes: the trend of distribution network
Sequence constraint, position voltage constraint and branch current constraint are invested to build in constraint, distributed generation resource；Described in wherein:
(3.1) trend constraint of distribution network
In formula, Pi、QiActive and reactive injecting power respectively at node i；Ui、UjRespectively node i, j voltage magnitude；
Gij、BijThe respectively conductance of branch ij, susceptance；θijThe phase difference of voltage between node i, j；
(3.2) distributed generation resource invests to build sequence constraint
In formula, βi,tWith γi,tRespectively indicate group number of the t stage in node i photovoltaic and blower of planning；βi,t+1With
γi,t+1Respectively indicate group number of the t stage in node i photovoltaic and blower of planning；
During the constraint condition indicates the multi-period planning of distributed generation resource, the photovoltaic or blower of next stage node i
Group number cannot be less than on last stage, i.e., any node distributed generation resource cannot remove after investing to build；
(3.3) node voltage constrains
Uimin< Ui< Uimax (12)
In formula, Uimin、UimaxThe respectively lower and upper limit of node i voltage value；
The constraint condition indicates that during carrying out more scene timing Power flow simulations with OpenDSS, any moment is each
The voltage magnitude of node must be between the safe bound of permission；
(3.4) branch current constrains
In formula, IkIndicate actual branch current,Indicate the upper limit of branch current value；
The constraint condition indicates that the branch current at any moment is no more than the electricity that the branch allows during Power flow simulation
Flow maximum value.
4) objective function is optimized using particle swarm algorithm in constraint condition, including particle swarm algorithm coding,
Processing for constraint condition and the solution based on particle swarm algorithm and OpenDSS.
The present invention optimizes the optimal access model of distributed generation resource using particle swarm algorithm, and particle swarm algorithm is adopted
Concurrently noninferior solution is scanned for efficient cluster, and can produce multiple noninferior solutions in iterative process every time；Simultaneously
Particle swarm algorithm has memory function, and particle is scanned for by tracking itself history optimal solution and population globally optimal solution,
It ensure that convergence and ability of searching optimum of the algorithm in searching process, therefore often by expanded application in distribution network planning problem
In.The particle swarm algorithm encodes
Segment encoding mode is used to optimization object, coded format D of each particle in search space is indicated are as follows:
In formula, the preceding N of DPVA variableIndicate number of the first stage photovoltaic of planning under each node to be selected；Indicate number of the planning first stage distribution blower under each node to be selected；Indicate planning
Number of the second stage photovoltaic of phase under each node to be selected；Indicate second stage blower each to be selected
Number under node, and so on until reach planning stage sum T；Particularly, since particle is in the location variable of each dimension
diWith eiIt is necessary for integer, therefore after particle updates oneself position with reference to itself locally optimal solution and globally optimal solution, it need to be into
Row is rounded downwards, to meet the discretization requirement of distributed generation resource installation number；
The processing for constraint condition, if being to be unsatisfactory for distributed generation resource after updating particle position to invest to build sequence about
The distributed generation resource number of beam, i.e., same node the latter half is less than previous stage, then forces the Node distribution formula power supply number
Mesh is set as identical as previous stage；If being unsatisfactory for node voltage constraint and branch current constraint, supplemented in objective function
Penalty term, respectively such as penalty term h given below1(t)、h2(t), it if meeting node voltage constraint and branch current constraint, will punish
Zero setting is penalized,
In formula, MUWith MIRespectively indicate the penalty coefficient U of voltage out-of-limit and electric current more in limited timeimin、UimaxRespectively node i
The lower and upper limit of voltage value；UiFor the virtual voltage amplitude of node i；IkIndicate actual branch current,Indicate branch electricity
The upper limit of flow valuve.
More scene timing Load flow calculations based on OpenDSS emulation platform will be embedded into particle swarm algorithm by the solution of model
In, and by constantly calling OpenDSS to carry out Power flow simulation come calculating target function and constraint condition and adjusting particle position, directly
To finding model optimal solution.In conjunction with particle swarm algorithm coding and the processing for constraint condition, obtain based on particle swarm algorithm
The Optimization Solution of the optimal access model of distributed generation resource.The solution based on particle swarm algorithm and OpenDSS, comprising:
(4.1) distribution network parameters and particle swarm algorithm parameter are inputted, wire topologies and resistance, distribution are specifically included
Power supply node to be selected, the aceleration pulse in particle swarm algorithm, inertial factor, constraint factor, while inputting distributed photovoltaic, wind
The parameter of machine, load cell obtains the distributed generation resource power output sequence and load value of each typical case's day under the more scenes of different phase；
(4.2) population is initialized, determines initial multi-period distributed generation resource access scheme；
(4.3) OpenDSS emulation platform is combined to carry out timing tide to the corresponding distributed generation resource programme of each particle
Stream calculation, obtain more scenes it is multi-period under voltage, network loss, power distribution information, calculate each node voltage and branch current；
(4.4) judge whether to meet node voltage constraint and branch current constraint, if being unsatisfactory for node voltage constraint and branch
Road restriction of current, then supplementary type (15) and penalty term shown in (16) in objective function, constrain and prop up if meeting node voltage
Road restriction of current, by penalty term zero setting；
(4.5) combined objective function target function value corresponding with penalty term each particle of calculating, as particle fitness, obtain
To the optimal value of each particle itself and the global optimum of population；
(4.6) judge whether particle swarm algorithm meets termination condition, i.e., whether global optimum restrains or reach maximum and change
Generation number is to export optimal solution and decode to obtain multistage distributed generation resource programme, otherwise enters in next step；
(4.7) carry out population and update operation, obtain new population Position And Velocity, when update adjustment particle coding with
The discretization for meeting distributed generation resource number requires and invests to build the constraint of sequence, and returns to (4.3) step.
Specific example is given below:
(1) typical scene and parameter setting
The present invention carries out simulation analysis to IEEE33 node typical examples, and system structure is as shown in Figure 4.System includes 32 altogether
Branch and 33 nodes set the distance between adjacent node as 0.5km, and interior joint 1 is bus nodes, electric power network head end
Reference voltage is 12.66kV.7th, 8,21,22 nodes be that distributed blower candidate installs node, the 18th, 21,30 nodes be point
Cloth photovoltaic both candidate nodes.
Project period is set as 20 years, is divided into four planning stages, with 5 years, 10 years and 15 years for line of demarcation, follow-up phase
Load growth rate compared with the initial stage is respectively 63%, 176% and 260%, and load growth trend is S type curve.Distribution
In terms of formula photovoltaic parameter: the parameter R of photovoltaic power output modelcAnd GstdRespectively 0.15kW/m2And 1kW/m2, single group photovoltaic it is specified
Power is 100kW, and the investment cost of unit capacity is 1.05 ten thousand yuan/kW, and maintenance cost ratio is 10%, residual value cost ratio
4%.In terms of fan parameter: incision, specified and cut-out wind speed is respectively 3,13,20m/s, separate unit blower rated power 100kW, single
Bit capacity investment cost is 0.9 ten thousand yuan/kW, maintenance cost ratio 12%, residual value cost ratio 6%.Set photovoltaic and blower
Service life is 30 years, discount rate 0.06.
The power purchase expense of power distribution network is 0.35 yuan/kWh, and the typical day number of days of four class scene of spring, summer, autumn and winter is respectively 87,
121,73 and 84, the permitted range of node voltage is 0.9~1.1 (per unit value), and the maximum allowed current of all branches is
1.2kA.Population population scale takes 20, sets maximum number of iterations as 100.
(2) impact analysis of distributed generation resource access
After example system access distributed photovoltaic and blower, timing Power flow simulation is carried out with OpenDSS platform, calculates DG
Network loss, the situation of change of voltage after access are the premises for realizing optimal access model Optimization Solution.
1) distributed generation resource accesses the influence to node voltage
By taking photovoltaic as an example, under initial load, distributed photovoltaic is accessed in 18 nodes of line end, and be step with 100kW
Length gradually increases photovoltaic capacity, that is, increases the photovoltaic group number of access, record the time-sequential voltage value of each node.Choose spring, summer, autumn and winter four
Totally 96 hours carry out timing simulation a typical case's day, obtain the access of distribution-free formula photovoltaic and 18 node voltages just more in limited time
Voltage timing difference is as shown in figs. 5 and 6.
In figure x-axis represent 1-33 node, y-axis as unit of hour time, z-axis is voltage per unit value.The face xoz is certain
The voltage's distribiuting situation of each node of a period of time discontinuity surface, the face yoz are the voltage timing variations situation of a certain node.
As seen from the figure, after distributed generation resource access, although the voltage change degree of each node difference in power distribution network
Node voltage is in lifting trend, even larger than network voltage, this is because the capacity of DG is far longer than the node at this time
Load power.When photovoltaic capacity is 1200kW, the voltage per unit value of 18 nodes reaches the upper limit 1.1.
2) distributed generation resource accesses the influence to network loss
Under initial load, distributed blower successively is accessed in node 2,9,18, is gradually increased since 0 using 200kW as step-length
Add, calculates whole year total network loss of system under different capabilities, obtain total network loss with change curve such as Fig. 7 institute of blower access capacity
Show.
As shown in Figure 7, with the increase of fan capacity, the trend for first reducing and increasing afterwards is presented in total network loss of system,
Reason is: with the access of distributed generation resource, the electric energy that DG is issued can directly feed the sub-load of the route, certain journey
System is reduced on degree to transfers loads active power, network loss also decreases；But with the distributed generation resource of access power distribution network
Continue to increase, the backward power from distributed power access point to electrical grid transmission will be will appear on route, this power can be with
The raising of distributed generation resource capacity and increase, therefore total network loss is gone up；When distributed generation resource capacity is more than certain value, point
The power that cloth blower is sent to power grid will be more than the performance number of power grid forward direction conveying when not accessing DG, therefore network loss can also surpass
Cross respective value when not accessing DG.
And the corresponding total network loss of system in more different on-positions, it can be gathered that following rule: it is first that access point is located at route
When end, total network loss slope of a curve is minimum, and when access point is located at end, total network loss slope of a curve is maximum, i.e., access point is located at
When route head end, it is maximum to reduce the fan capacity accessed required for the network loss of same units, when corresponding network loss reaches minimum and
Blower access capacity when more than the network loss value for not accessing DG also highest.Distributed power access point be located at route head end, middle-end,
When end, the weakening effect of network loss is successively enhanced.
The capacity of DG access and position can the voltage to example system had an impact with network loss, need in conjunction with being based on
The timing tidal current analysis of OpenDSS determines reasonable optimal access scheme.
(3) optimum results of optimal access model
It is programmed using MATLAB and carries out particle group optimizing, and constantly OpenDSS is called to carry out Power flow simulation, calculate target letter
Several and constraint condition, optimizes the optimal access model of DG in conjunction with the mentioned method of Section 3, obtains the different planning stages
The optimal access scheme of DG is as shown in table 1.
Table 1DG multistage optimal access scheme
As shown in Table 1, institute's climbing form type of the present invention can realize the multistage programming of distributed blower and photovoltaic in power distribution network.Knot
The load development level of different phase is closed, the optimal access scheme of DG adaptable therewith is determined, to realize that power distribution network developed
The flexible dynamic access of DG in journey.
Further the target year comprehensive cost final value under more different programmes, concrete scheme are as follows:
Scheme 1: DG is not installed
Scheme 2: blower is only installed；
Scheme 3: photovoltaic is only installed；
Scheme 4: more scene temporal characteristics of load are not considered；
Scheme 5: do not consider multistage programming；
Scheme 6: neither consider more scene temporal characteristics of load nor consider multistage programming；
Scheme 7: being suggested plans herein, i.e., considers multi-period more scenes simultaneously.
Target year program results under different schemes are as shown in table 2:
The comparison of 2 programme of table
Comparison scheme 1,2,3 and scheme 7, it can be found that blower is planned with photovoltaic simultaneously can reduce it is final comprehensive
Conjunction expense.Although having certain construction O&M expense after installation DG, reasonably selecting capacity can be effectively reduced network loss and net confession
Load, to promote economy on the whole, while the complementary characteristic of blower and photovoltaic can also improve the trend distribution of network.
Therefore, it is very necessary for DG rationally being accessed in power distribution network.
Comparison scheme 4 and 7 it can be found that consider load more scene temporal characteristics after target function value it is significantly superior,
This is because considering wind speed, light intensity and load in the complementarity and coupling of different periods.And scheme 5 and scheme 7 are compared,
Under multistage Dynamic Programming, project period, the reducing effect of comprehensive cost was become apparent, program results more application of load actual electric network
Development.The comparison of different schemes demonstrates multi-period more optimal accesses of scene distribution formula power supply that the present invention is proposed in table
The reasonability and validity of position and capacity research method.
Claims (7)
1. a kind of multi-period more optimal on-positions of scene distribution formula power supply and capacity research method, which is characterized in that including such as
Lower step:
1) component models, including distributed photovoltaic model, distributed blower model and load model are established；
2) establish objective function, be with the minimum objective function of comprehensive cost final value in project period, including distributed blower and
Investment, O&M expense and the residual value of photovoltaic and the power purchase expense of power distribution network superior power grid；
3) constraint condition is established, constraint condition includes: that the trend constraint of distribution network, distributed generation resource invest to build sequence constraint, position
Set voltage constraint and branch current constraint；
4) objective function is optimized using particle swarm algorithm in constraint condition, including particle swarm algorithm coding, for
The processing of constraint condition and solution based on particle swarm algorithm and OpenDSS.
2. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, described in step 1),
(1.1) distributed photovoltaic model: the power output of photovoltaic depends primarily on intensity of illumination, in the premise of more scene timing simulations
Under, photovoltaic power output and the relationship of intensity of illumination are distributed photovoltaic model, it is expressed as follows:
In formula: PbIt is the real-time power output of photovoltaic；PsnIndicate the rated power of photovoltaic；GstdIndicate specified intensity of illumination；RcIt indicates to appoint
The relationship of the light intensity of one certain strength, i.e. photovoltaic power output and light intensity is by non-linear to linear turning point；GbτIndicate the τ hour
Real-time light intensity；
(1.2) distributed blower model, as the power output P of wind-driven generatorwindIt is as follows with the functional relation of wind speed v:
Wherein, PwindmaxFor the rated power of blower；vnFor the incision wind speed of blower；vrFor the rated wind speed of blower；voFor blower
Cut-out wind speed；
(1.3) load model is expressed as follows:
Lt=Lp×Ps(t) (3)
In formula, LtFor any hour workload demand amount, LpFor year load peak；PsIt (t) is the negative of each hour under s-th of scene
The proportionality coefficient of lotus and year load peak.
3. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, objective function described in step 2) are as follows:
MinC=CPV+CWTG+COP (4)
In formula, CPVIndicate the overall life cycle cost final value of distributed photovoltaic, CWTGIndicate the life cycle management of distributed blower at
This final value, COPIndicate the power purchase expense final value of power distribution network superior power grid in project period；CPVWith CWTGCalculation formula it is as follows:
In formula, T is planning stage sum；NPVWith NWTGRespectively indicate the node total number yet to be built of photovoltaic and blower in power distribution network；RPVWith
RWTGRespectively indicate single group photovoltaic or blower invests to build cost；βi,tWith γi,tThe t stage of planning is respectively indicated in node i photovoltaic
With the group number of blower；R is social discount rate；M and n respectively indicates the service life of photovoltaic and blower；upvWith uwtgRespectively indicate photovoltaic and
The maintenance cost ratio of blower；zpvWith zwtgRespectively indicate the residual value cost ratio of photovoltaic and blower；ItFor final value conversion coefficient,
Calculation method is as follows:
In formula, ntFor t-th of stage year, atFor the initial time in t-th of stage, aTThe time is terminated for project period；
The power purchase expense final value of power distribution network superior power grid is expressed as follows in project period:
In formula, SendFor year scene sum, including four typical scenes of spring, summer, autumn and winter；τendIndicate the hourage under any scene；CP
Indicate electricity price；WtsτThe purchase of electricity for indicating τ moment power distribution network superior power grid under s-th of scene of t stage, by load, distribution
Power supply generated energy and route network loss three parts composition, calculation formula are as follows:
In formula, htIndicate load growth rate of the t stage compared with planning the starting year；Ls,τ,kIt indicates under s scene k-th of the τ moment
The initial load of load point；K indicates load point set；Pb(s, τ) indicates the real-time power output of τ moment single group photovoltaic under s scene, root
It is calculated according to distributed photovoltaic model；Pwind(s, τ) indicates the real-time power output of τ moment single group blower under s scene, according to distributed wind
Machine model calculates；JiIndicate distribution network line set；Pj,s,τWith Qj,s,τRespectively the τ moment flows through having for route j head end under s scene
Function, reactive power；Uj,s,τFor route j first section voltage；RjIndicate the resistance of route j.
4. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, described in step 3):
(3.1) trend constraint of distribution network
In formula, Pi、QiActive and reactive injecting power respectively at node i；Ui、UjRespectively node i, j voltage magnitude；Gij、Bij
The respectively conductance of branch ij, susceptance；θijThe phase difference of voltage between node i, j；
(3.2) distributed generation resource invests to build sequence constraint
In formula, βi,tWith γi,tRespectively indicate group number of the t stage in node i photovoltaic and blower of planning；βi,t+1With γi,t+1Point
Group number of the t stage in node i photovoltaic and blower of planning is not indicated；
During the constraint condition indicates the multi-period planning of distributed generation resource, the photovoltaic or blower fan group number of next stage node i
It cannot be less than on last stage, i.e., any node distributed generation resource cannot remove after investing to build；
(3.3) node voltage constrains
Uimin< Ui< Uimax (12)
In formula, Uimin、UimaxThe respectively lower and upper limit of node i voltage value；
The constraint condition indicates, during carrying out more scene timing Power flow simulations with OpenDSS, any moment each node
Voltage magnitude must be between the safe bound of permission；
(3.4) branch current constrains
In formula, IkIndicate actual branch current,Indicate the upper limit of branch current value；
The constraint condition indicates, the electric current that the branch current at any moment allows no more than the branch during Power flow simulation is most
Big value.
5. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, the coding of particle swarm algorithm described in step 4) includes:
Segment encoding mode is used to optimization object, coded format D of each particle in search space is indicated are as follows:
In formula, the preceding N of DPVA variable d1~dNPVIndicate number of the first stage photovoltaic of planning under each node to be selected；Indicate number of the planning first stage distribution blower under each node to be selected；Indicate planning
Number of the second stage photovoltaic of phase under each node to be selected；Indicate second stage blower each to be selected
Number under node, and so on until reach planning stage sum T；Particularly, since particle is in the location variable of each dimension
diWith eiIt is necessary for integer, therefore after particle updates oneself position with reference to itself locally optimal solution and globally optimal solution, it need to be into
Row is rounded downwards, to meet the discretization requirement of distributed generation resource installation number.
6. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, for the processing of constraint condition described in step 4), if being to be unsatisfactory for distributed generation resource after updating particle position
Sequence constraint is invested to build, i.e., the distributed generation resource number of same node the latter half is less than previous stage, then forces the node point
Cloth power supply number is set as identical as previous stage；If node voltage constraint and branch current constraint are unsatisfactory for, in target
Penalty term is supplemented in function, respectively such as penalty term h given below1(t)、h2(t), if meeting node voltage constraint and branch electricity
Stream constraint, by penalty term zero setting,
In formula, MUWith MIRespectively indicate the penalty coefficient of voltage out-of-limit and electric current more in limited time；Uimin、UimaxRespectively node i voltage
The lower and upper limit of value；UiFor the virtual voltage amplitude of node i；IkIndicate actual branch current,Indicate branch current value
The upper limit.
7. multi-period more optimal on-positions of scene distribution formula power supply according to claim 1 and capacity research method,
It is characterized in that, the solution based on particle swarm algorithm and OpenDSS, comprising:
(4.1) distribution network parameters and particle swarm algorithm parameter are inputted, wire topologies and resistance, distributed generation resource are specifically included
Node to be selected, the aceleration pulse in particle swarm algorithm, inertial factor, constraint factor, while inputting distributed photovoltaic, blower, negative
The parameter of lotus element obtains the distributed generation resource power output sequence and load value of each typical case's day under the more scenes of different phase；
(4.2) population is initialized, determines initial multi-period distributed generation resource access scheme；
(4.3) OpenDSS emulation platform is combined to carry out timing trend meter to the corresponding distributed generation resource programme of each particle
Calculate, obtain more scenes it is multi-period under voltage, network loss, power distribution information, calculate each node voltage and branch current；
(4.4) judge whether to meet node voltage constraint and branch current constraint, if being unsatisfactory for node voltage constraint and branch electricity
Stream constraint, then supplementary type (15) and penalty term shown in (16) in objective function, constrain and branch electricity if meeting node voltage
Stream constraint, by penalty term zero setting；
(4.5) combined objective function target function value corresponding with penalty term each particle of calculating, as particle fitness obtain each
The optimal value of particle itself and the global optimum of population；
(4.6) judge whether particle swarm algorithm meets termination condition, i.e., whether global optimum restrains or reach greatest iteration time
Number is to export optimal solution and decode to obtain multistage distributed generation resource programme, otherwise enters in next step；
(4.7) it carries out population and updates operation, obtain new population Position And Velocity, adjustment particle coding is to meet when update
The discretization of distributed generation resource number requires and invests to build the constraint of sequence, and returns to (4.3) step.
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