CN110428103A - A kind of renewable energy energy-storage system collaborative planning method in integrated energy system - Google Patents
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Abstract
The invention discloses a kind of renewable energy energy-storage system collaborative planning methods in integrated energy system, include the following steps: for multiple target, at many levels, multiple constraint and conjunction integral nonlinear optimization problem, the permeability using the desired value of totle drilling cost and RES under several typical scenes is as optimization aim again, and consider the uncertainty of collaboration optimization, establish active distribution system collaborative planning model, the present invention, which establishes, considers RES, the ADS collaborative planning model of ESS and distribution network, and model can be from the foundation of typical daily scene and the foundation of the ADS plan model optimized based on multi-objective and multi-hierarchy when establishing, wherein the foundation of typical daily scene can clearly indicate final time sequencing relevant to ESS operation, and every level of ADS plan model has respective target and decision space , i.e., its can find optimization scheme from the problem of ADS operation cost and DGO net profit under the distribution of the network planning, RES and ESS and different scenes.
Description
Technical field
The present embodiments relate to renewable energy in ENERGY PLANNING technical field more particularly to a kind of integrated energy system
Energy-storage system collaborative planning method.
Background technique
Integrated energy system, which refers to, utilizes advanced physical message technology and Innovative Management Mode in certain area, integrate area
The various energy resources such as coal, petroleum, natural gas, electric energy, thermal energy in domain realize the coordination rule between a variety of heterogeneous energy subsystems
It draws, optimization operation, coordinated management, interaction response and complementary mutually Ji.While meeting diversification energy demand in system, to have
Effect ground promotes efficiency of energy utilization, promotes the energy resource system of the novel all-in-one of energy sustainable development, Renewable Energy Development
The core content of Shi Xianjin Energy restructuring and important channel, in order to consider energy-storage system, there has been proposed about power distribution network
Optimization planning scheme or method.
Occur many distribution network planning methods for considering energy storage at present, however, some keys in distribution network planning are asked
Topic is not still resolved, for example, all concentrated on about the optimization problem of energy-storage system in the planning of energy-storage system at present, but
Most of researchs all do not account for the operation problem of energy-storage system, and for another example most of plan models tend to only consider economy
As optimization aim, though the other standards such as reliability the considerations of have, but or be a limit by other standards simple process
Condition processed or economic indicator is converted by other standards subjectivity, these processing methods can not be examined in the angle of global optimum
Consider the influence of other targets, that is, ignore the different objects of planning of multiple stakeholder, limitation is larger.
Summary of the invention
For this purpose, the embodiment of the present invention provides renewable energy energy-storage system collaborative planning side in a kind of integrated energy system
Method, with solve in the prior art due to most of plan model processing methods can not from the point of view of global optimum other mesh
Target influences, that is, ignores the different objects of planning of multiple stakeholder, the larger problem of limitation.
To achieve the goals above, embodiments of the present invention provide the following technical solutions:
A kind of renewable energy energy-storage system collaborative planning method in integrated energy system, includes the following steps:
S100, multi-objects and multi-layers, multiple constraint are directed to and closes integral nonlinear optimization problem, with the expection of totle drilling cost
Value and permeability of the RES under several typical scenes are as optimization aim, the planning and fortune of collaboration optimization different time scales
Uncertainty when row establishes active distribution system collaborative planning model；
S200, active distribution system collaborative planning model is solved using improved particle swarm optimization algorithm, and with
The maximum of economy and renewable energy permeability turns to target, to obtain optimal solution.
As a preferred solution of the present invention, the active distribution system collaborative planning model established in the step S100
It include: the foundation of typical daily scene and the ADS plan model based on multi-objective and multi-hierarchy optimization.
As a preferred solution of the present invention, active distribution system collaborative planning model packet is established in the step S100
Include following steps: collaborative planning model framework；Optimization object function；For the operation constraint of equipment investment constraint, network and ESS
It is proposed corresponding constraint condition.
As a preferred solution of the present invention, the best side that optimization aim passes through multi-target method in the step S100
Case makes the net profit of DGO maximize and obtain more income building optimization object functions, and the optimization aim includes:
Upper layer target, middle layer target and lower layer's target；
The upper layer target is to carry out planning network from the angle of DSO, is examined from two targets of economy and RES permeability
Consider, to obtain preferred plan；
The building concrete mode of economy objectives:
Economic goal function is constructed by cost of investment, operation and maintenance cost, which can use formula
(1)-(7) it indicates:
In formula,Feeder line day fund cost is represented,For feeder line day operation maintenance cost,For
Network operation cost under scene sc, ηscFor scene sc probability of happening, NscFor scene sc number；
In formula, ΩRF,ΩAFReplacement feed line and additional feed line are respectively represented,Respectively fed with replacement
Line A and the relevant variable of existing feed line B,For replace feed line A day capital cost,For additional feed
The day capital cost of line B, δFFor feeder line rate of recovery of capital；
In formula, ΩEFFor existing feed line collection,For replace feed line A operation and maintenance cost,For
The operation and maintenance cost of additional feed line B,For variable relevant to existing feed line C,For existing feed line C
Day operation maintenance cost；
In formula, EPtFor tou power price t, EPPV,EPWGRespectively photovoltaic contract electricity price and wind-powered electricity generation contract electricity price,For
In scene sc when time t photovoltaic n active output,For in scene sc when time t wind-powered electricity generation m active output；
In formula, ΩbusTo load bus group,For in scene sc, the burden with power demand of bus i when time t,
In scene sc, the active output of ESS k when time t,Active via net loss in scene sc, when time t；
In formula, Ia,sc,t,Ib,sc,tIc,sc,tIn respectively scene sc, the electric current of feeder line A/B/C, R when time ta,Rb,RcFor
Not Wei feeder line A/B/C resistance value；
In formula,For controller switching equipment rate of recovery of capital, r is interest rate,For the controller switching equipment service life；
The building concrete mode of RES permeability target:
Alleviate network congestion by building RES permeability function optimization network structure, to improve RES's to the maximum extent
Permeability, the RES permeability function can be indicated with formula (8)-(10):
In formula, RESPscFor the RES permeability in scene sc,For the energy provided in scene sc by RES,
For the workload demand energy in scene sc；
In formula,For in scene sc, the active output of photovoltaic n when time t,For in scene sc, when time t
The active output of wind-powered electricity generation m；
In formula,For in scene sc, the burden with power demand of bus i when time t,In scene sc, time t
When active via net loss.
As a preferred solution of the present invention, the middle layer target:
The configuration for optimizing RES and ESS from the angle of DGO is optimized by constructing middle layer objective function, makes DGO's
Net profit maximizes, which can be indicated with formula (8)-(10):
In formula,For the day net profit of DGO in scene sc；
In formula,The day net profit of PV n/WG m/ESS k in respectively scene sc；
In formula (13)-(15),The day benefit of PV n/WG m/ESS k in difference scene sc,
Respectively PV n/WG m days totle drilling cost,For the day totle drilling cost of ESS k in scene sc；
In formula (16)-(21), ccPV,ccWGThe unit capital cost (dollar/kw) of respectively PV/WG, ccPCS,ccB&RPoint
Not Wei ESS unit capital cost relevant to power conversion system/storage cavern, cmPV,cmWGRespectively PV/WG unit O&M cost,
cmFOM,cmVOMRespectively ESS unit fixes/variable operation maintenance cost,Respectively PV n/WG m's is specified
Power；
Lower layer's target: being to carry out optimal scheduling to ESS based on the considerations of typical scene, by constructing lower layer's target letter
Number accounts for, to obtain more incomes, which is formulated are as follows:
As a preferred solution of the present invention, the constraint condition includes:
Installed capacity limitation: in order to mitigate bi-directional current bring adverse effect, the installed capacity ratio of RES should be less than permitting
Perhaps it is worth, the installed capacity of the RES indicates:
In formula, λIAllow maximum value, S for RES permeabilitysubFor high, Zhong Ya electric substation rated power；
Network operation constraint: the network operation constraint includes active/reactive power equilibrium equation, power flow equation and electricity
The security constraint of stream and voltage, the network operation constraint representation:
In formula,In respectively scene sc when time t high pressure/middle buckling power station it is active/reactive power output,
Pi,sc,t,Qi,sc,tIn respectively scene sc in time t bus i active/reactive power, Ui,sc,t,θij,sc,tRespectively scene
Voltage amplitude/voltage angular difference in sc when time t between bus i and bus j, Gij,BijBetween respectively bus I and bus J
Transfer conductance/susceptance,Respectively bus I voltage amplitude allowed band,Respectively feeder line A/B/
The maximum current of C；
The operation of ESS constrains, and the operation of ESS strictly should be allowed model by cyclic behaviour, state-of-charge and charge-discharge electric power
The limitation enclosed, the operation constraint representation of the ESS:
Socmin≤Sock,sc,t≤Socmax (32)
In formula,For the rated power of ESS k, Socmax,SocminFor state-of-charge allowed band,For field
In scape sc, the energy being stored in time t in ESS k battery pack, ηC,ηDESS efficiency for charge-discharge respectively.
As a preferred solution of the present invention, detailed process is as follows for the foundation of the daily scene of typical case:
Firstly, the year prediction data of wind speed, solar irradiation and workload demand is unified by respective maximum value and minimum value
Change, unitized year related data is then divided into 365 day section, establishes initial day scene matrix；
Secondly, 365 day section is clustered into typical day section by K- mean cluster, in order to ensure selected typical case
The quality and diversity of everyday scenes, number of clusters by Davies Bouldin Effective exponent IDBIt determines, later, by matrix
SinitialBe converted to typical daily scene matrix Sclustered, IDBIt indicates are as follows:
Rgh=(Sg+Sh)/dgh (35)
dgh=| | cg-ch|| (37)
In formula, IDBFor Cluster Validity index, NCFor k means Clustering number, NgFor cluster centre vector number g, dghFor
The distance between cluster centers G and cluster centers H, Sg,ShRespectively cluster centers dispersion degree g/ cluster centers h, cg,chRespectively
For cluster centers G/ cluster centers H；
Then variable after Matrix Cluster is restored to original boundary, be used for optimization planning scheme.
As a preferred solution of the present invention, improved particle swarm optimization algorithm includes such as lower section in the step s200
Method:
(1) Tent chaotic maps: in order to avoid pseudo-random number sequence, using ergodic and the better Tent chaos of randomness
Mapping generates initial population；
(2) combination of genetic algorithm and particle swarm algorithm: in modified particle swarm optiziation, the quick non-row of being dominant is introduced
The quick non-sort method that is dominant used in sequence genetic algorithm, meanwhile, it is distributed according to the crowding distance of non-domination solution, selection is each
The globally optimal solution of iteration；
(3) integer programming based on wheel disc algorithm: the integer programming in middle layer in consideration, position of each particle in solution space
It sets and must be converted into integer variable, integer programming is carried out using wheel disc algorithm.
As a preferred solution of the present invention, improved particle swarm optimization algorithm is in processing upper layer target, middle layer target
With three kinds of different optimization process, and the specific optimization process of improved particle swarm optimization algorithm can be used when lower layer's target respectively
It is as follows:
Firstly, the multi-objective particle swarm algorithm based on Pareto, generates upper layer initial population using tent chaotic maps；
Then, middle layer is sent by the upper layer of particles for representing candidate network structure, at this time in candidate network structure, mixed
Ignorant particle swarm algorithm produces middle layer initial population；
Later, the middle layer particle for representing RES and ESS candidate allocation scheme and specified network are sent collectively to lower layer,
Therefore, Chaos particle swarm optimization algorithm can optimize the scheduling of each ESS under particular network condition, and to RES's and ESS
Distribution optimizes；
Then the traffic control of obtained each ESS is fed back to middle layer, calculates the optimization of the candidate allocation scheme
Target, middle layer optimization after, by after optimization allocation plan and job scheduling feed back to upper layer, calculate the object of planning of DSO；
Identical step is finally repeated, until the multiple target PSO convergence based on Pareto.
As a preferred solution of the present invention, the ADS plan model based on multi-objective and multi-hierarchy optimization specifically wraps
It includes: the middle layer model and use of the assignment problem for optimizing the upper layer model of network planning issue, for solving RES and ESS
In the ESS traffic control for optimizing each typical scene, upper layer and middle layer are fed back to, calculates the ADS fortune under different scenes per hour
Seek the underlying model of cost and the net profit of DGO.
Embodiments of the present invention have the advantages that
(1) present invention establish one the considerations of integrating multi-objects and multi-layers RES, ESS and distribution network ADS
Collaborative planning model is examining the desired value of totle drilling cost and RES in multiple permeabilities in typical case as optimization aim
The planning and operation of collaboration optimization different time scales have been substantially ensured in the case where considering uncertainty, and model can when establishing
From the foundation of typical daily scene and the foundation of the ADS plan model optimized based on multi-objective and multi-hierarchy, wherein typical
The foundation of daily scene can directly make computation burden be mitigated and meanwhile will be appreciated also that expression and relevant final ESS operation when
Between sequence, and each level of ADS plan model has respective target and decision space, i.e., it can be from the network planning, RES
The problem of distribution with ESS and ADS operation cost and DGO net profit under different scenes, which sets out, finds optimization scheme,
Limitation is lower；
(2) present invention has used the particle swarm algorithm improved during also resettling model, it is made to generate initial kind
It can directly be generated by the Tent chaotic maps with better ergodic and randomness when group, later also in improved population
In algorithm, the quick non-quick non-sort method that is dominant used in Sorting Genetic Algorithm that is dominant is introduced, makes it can in processing
The globally optimal solution of each iteration is selected, while it is whole also based on wheel disc algorithm position of the particle in solution space to be converted into
When number, available most accurate integer.
Detailed description of the invention
It, below will be to embodiment party in order to illustrate more clearly of embodiments of the present invention or technical solution in the prior art
Formula or attached drawing needed to be used in the description of the prior art are briefly described.It should be evident that the accompanying drawings in the following description is only
It is merely exemplary, it for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer, which is extended, obtains other implementation attached drawings.
Structure depicted in this specification, ratio, size etc., only to cooperate the revealed content of specification, for
Those skilled in the art understands and reads, and is not intended to limit the invention enforceable qualifications, therefore does not have technical
Essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influencing the function of the invention that can be generated
Under effect and the purpose that can reach, should all still it fall in the range of disclosed technology contents obtain and can cover.
Fig. 1 is collaborative planning illustraton of model of the invention；
Fig. 2 is the optimized flow chart of optimum programming scheme of the present invention；
Fig. 3 is flow chart of the present invention.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily, it is clear that described embodiment is the present invention one
Section Example, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
As shown in figures 1 and 3, the present invention provides renewable energy energy-storage system collaboration rule in a kind of integrated energy system
The method of drawing, includes the following steps:
S100, multi-objects and multi-layers, multiple constraint are directed to and closes integral nonlinear optimization problem, then with the pre- of totle drilling cost
The permeability of time value and RES under several typical scenes considers collaboration optimization different time scales as optimization aim
Uncertainty when planning and operation, establishes active distribution system collaborative planning model.
The illumination that several typical scenes are represented by several workload demands, the wind speed of wind energy and light energy source is strong
The typical everyday scenes such as degree.
After obtaining integrated energy system middle age power consumption/generated energy relevant information, in order to mitigate computation burden, catch
Hour variability relevant to renewable energy (RES) and workload demand and uncertainty are obtained, with k- means clustering algorithm by year
Power consumption/generated energy time series is divided into several typical scenes, these typical everyday scenes remain wind speed, too
Sunlight according to and workload demand time sequencing, so as to indicate final time sequencing relevant to ESS operation, and from more mesh
Mark, multi-level angle are set out, and the optimization operation in combination with distribution and ESS to the network planning, RES and ESS can be built
The ADS plan model of the multi-objective and multi-hierarchy that is based on optimization, the ADS plan model that will be optimized again based on multi-objective and multi-hierarchy later
Just active distribution system collaborative planning model, i.e. multi-objects and multi-layers optimization planning are formd after in conjunction with typical everyday scenes
Model, and three models are respectively provided in the ADS plan model based on multi-objective and multi-hierarchy optimization, model is cooperateed with
Optimize the planning and operation of different time scales.
S200, active distribution system collaborative planning model is solved using improved particle swarm optimization algorithm, to answer
To the different objects of planning, to obtain optimal solution.
The active distribution system collaborative planning model established in the step S100 include: the foundation of typical daily scene with
And the ADS plan model based on multi-objective and multi-hierarchy optimization.
It establishes active distribution system collaborative planning model in the step S100 to be mainly unfolded from the following aspect: collaborative planning
Model framework；Optimization object function；Operation constraint for equipment investment constraint, network and ESS proposes corresponding constraint condition.
The active distribution system collaborative planning model first builds up collaborative planning model framework (i.e. by typical day when establishing
Reason scape and the ADS plan model optimized based on multi-objective and multi-hierarchy combine composition collaborative planning model framework), and then will
Objective function and constraint conditional plan it is good, once then user need to find preferred plan can be according to objective function and constraint
Formula expressed by condition is calculated.
Optimization aim is by considering the preferred plan of multi-target method, keeping the net profit of DGO maximum in the step S100
Change and obtain more incomes building optimization object functions, and the optimization aim include: upper layer target, middle layer target and under
Layer target；
The upper layer target is to carry out planning network from the angle of DSO, is examined from two targets of economy and RES permeability
Consider, to obtain preferred plan；
(1) economy objectives:
Economic goal function is constructed by cost of investment, operation and maintenance cost, which can use formula
(1)-(7) it indicates:
In formula,Feeder line day fund cost is represented,For feeder line day operation maintenance cost,For
Network operation cost under scene sc, ηscFor scene sc probability of happening, NscFor scene sc number；
In formula, ΩRF,ΩAFReplacement feed line and additional feed line are respectively represented,Respectively fed with replacement
Line A and the relevant variable of existing feed line B,For replace feed line A day capital cost,For additional feed
The day capital cost of line B, δFFor feeder line rate of recovery of capital；
In formula, ΩEFFor existing feed line collection,For replace feed line A operation and maintenance cost,For
The operation and maintenance cost of additional feed line B,For variable relevant to existing feed line C,For existing feed line C
Day operation maintenance cost；
In formula, EPtFor tou power price t, EPPV,EPWGPhotovoltaic/wind-powered electricity generation contract electricity price,For in scene sc, when time t
The active output of photovoltaic n,For in scene sc, the active output of wind-powered electricity generation m when time t；
In formula, ΩbusTo load bus group,For in scene sc, the burden with power demand of bus i when time t,
In scene sc, the active output of ESS k when time t,Active via net loss in scene sc, when time t；
In formula, Ia,sc,t,Ib,sc,tIc,sc,tIn respectively scene sc, the electric current of feeder line A/B/C, R when time ta,Rb,RcFor
It Wei not feeder line A/B/C resistance value；
In formula,For controller switching equipment rate of recovery of capital, r is interest rate,For the controller switching equipment service life；
(2) RES permeability target:
Alleviate network congestion by building RES permeability function optimization network structure, to improve RES's to the maximum extent
Permeability, the RES permeability function can be indicated with formula (8)-(10):
In formula, RESPscFor the RES permeability in scene sc,For the energy provided in scene sc by RES,
For the workload demand energy in scene sc；
In formula,For in scene sc, the active output of photovoltaic n when time t,For in scene sc, when time t
The active output of wind-powered electricity generation m；
In formula,For in scene sc, the burden with power demand of bus i when time t,In scene sc, time t
When active via net loss.
The middle layer target:
The configuration for optimizing RES and ESS from the angle of DGO is optimized by constructing middle layer objective function, makes DGO's
Net profit maximizes, which can be indicated with formula (8)-(10):
In formula,For the day net profit of DGO in scene sc；
In formula,The day net profit of PV n/WG m/ESS k in respectively scene sc；
In formula (13)-(15),The day benefit of PV n/WG m/ESS k in difference scene sc,Respectively PV n/WG m days totle drilling cost,For the day totle drilling cost of ESS k in scene sc；
In formula (16)-(21), ccPV,ccWGThe unit capital cost (dollar/kw) of respectively PV/WG, ccPCS,ccB&RPoint
Not Wei ESS unit capital cost relevant to power conversion system/storage cavern, cmPV,cmWGRespectively PV/WG unit O&M cost,
cmFOM,cmVOMRespectively ESS unit fixes/variable operation maintenance cost,Respectively PV n/WG m's is specified
Power；
Lower layer's target: being to carry out optimal scheduling to ESS based on the considerations of typical scene, by constructing lower layer's target letter
Number accounts for, to obtain more incomes, which is formulated are as follows:
The constraint condition includes:
(1) installed capacity limits
In order to mitigate bi-directional current bring adverse effect, the installed capacity ratio of RES should be less than permissible value, the RES's
Installed capacity can be indicated with formula (23):
In formula, λIAllow maximum value, S for RES permeabilitysubFor high, Zhong Ya electric substation rated power；
(2) network operation constrains
The network operation constraint includes the peace of active/reactive power equilibrium equation, power flow equation and electric current and voltage
Staff cultivation, the network operation constraint can be indicated with formula (24)-(29):
In formula (24)-(29),In respectively scene sc, high pressure/middle buckling power station is active/idle when time t
Power output, Pi,sc,t,Qi,sc,tIn respectively scene sc, active/reactive power of bus i, U in time ti,sc,t,θij,sc,t
In respectively scene sc, voltage amplitude/voltage angular difference when time t between bus i and bus j, Gij,BijRespectively bus I and
Transfer conductance/susceptance between bus J,Respectively bus I voltage amplitude allowed band,Point
Not Wei feeder line A/B/C maximum current；
(3) the operation constraint of ESS
The operation of ESS should be strictly by cyclic behaviour, state-of-charge (State of Charge, SOC) and charge-discharge electric power
The operation constraint of the limitation of allowed band, the ESS can be indicated with formula (30)-(33):
Socmin≤Sock,sc,t≤Socmax (32)
In formula (30)-(33),For the rated power of ESS k, Socmax, Socmin are SOC allowed band,For the energy in scene sc, being stored in time t in ESS k battery pack, ηC,ηDESS efficiency for charge-discharge respectively.
Detailed process is as follows for the foundation of the daily scene of typical case:
Firstly, the year prediction data of wind speed, solar irradiation and workload demand is unified by respective maximum value and minimum value
Change, unitized year related data is then divided into 365 day section, establishes initial day scene matrix；
Secondly, 365 day section is clustered into typical day section by K- mean cluster, in order to ensure selected typical case
The quality and diversity of everyday scenes, number of clusters by Davies Bouldin Effective exponent IDBIt determines, later, by matrix
SinitialBe converted to typical daily scene matrix Sclustered, IDBIt can be determined by formula (34)-(37):
Rgh=(Sg+Sh)/dgh (35)
dgh=| | cg-ch|| (37)
In formula (34)-(37), IDBFor Cluster Validity index, NCFor k means Clustering number, NgFor cluster centre vector
Number g, dghFor the distance between cluster centers G and cluster centers H, Sg,ShRespectively cluster centers dispersion degree g/ cluster centers h,
cg,chRespectively cluster centers G/ cluster centers H；
Then variable after Matrix Cluster is restored to original boundary, be used for optimization planning scheme.
Improved particle swarm optimization algorithm includes following corrective measure in the step s200:
(1) Tent chaotic maps: in order to avoid pseudo-random number sequence, using ergodic and the better Tent chaos of randomness
Mapping generates initial population；
(2) combination of genetic algorithm and particle swarm algorithm: in modified particle swarm optiziation, the quick non-row of being dominant is introduced
The quick non-sort method that is dominant used in sequence genetic algorithm, meanwhile, it is distributed according to the crowding distance of non-domination solution, selection is each
The globally optimal solution of iteration；
(3) integer programming based on wheel disc algorithm: the integer programming in middle layer in consideration, position of each particle in solution space
It sets and must be converted into integer variable, integer programming is carried out using wheel disc algorithm, which is not to be rounded up to numerical value
Immediate integer, but according to the distance between non-integer variable and all candidate integer variables come round numbers.
As shown in Fig. 2, the improved particle swarm optimization algorithm is when handling upper layer target, middle layer target and lower layer's target
Three kinds of different optimization process can be used respectively, lower layer's insertion middle layer is optimized into program in practical operation, middle layer is embedded in upper layer
Optimize program, and the specific optimization process of the improved particle swarm optimization algorithm is as follows:
Firstly, the multi-objective particle swarm algorithm based on Pareto, generates upper layer initial population using tent chaotic maps；
Then, middle layer is sent by the upper layer of particles for representing candidate network structure, at this time in candidate network structure, mixed
Ignorant particle swarm algorithm produces middle layer initial population；
Later, the middle layer particle for representing RES and ESS candidate allocation scheme and specified network are sent collectively to lower layer,
Therefore, Chaos particle swarm optimization algorithm can optimize the scheduling of each ESS under particular network condition, and to RES's and ESS
Distribution optimizes；
Then the traffic control of obtained each ESS is fed back to middle layer, calculates the optimization of the candidate allocation scheme
Target, middle layer optimization after, by after optimization allocation plan and job scheduling feed back to upper layer, calculate the object of planning of DSO；
Identical step is finally repeated, until the multiple target PSO convergence based on Pareto.
The ADS plan model based on multi-objective and multi-hierarchy optimization specifically includes: for optimizing network planning issue
Upper layer model, the middle layer model of assignment problem for solving RES and ESS and the ESS for optimizing each typical scene are transported
Row scheduling, feeds back to upper layer and middle layer, calculates the net profit (NPV) of the ADS operation cost and DGO under different scenes per hour
Underlying model.
Although above having used general explanation and specific embodiment, the present invention is described in detail, at this
On the basis of invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Therefore,
These modifications or improvements without departing from theon the basis of the spirit of the present invention are fallen within the scope of the claimed invention.
Claims (10)
1. a kind of renewable energy energy-storage system collaborative planning method in integrated energy system, which is characterized in that including walking as follows
It is rapid:
S100, for multi-objects and multi-layers, multiple constraint and close integral nonlinear optimization problem, with the desired value of totle drilling cost and
Permeability of the RES under several typical scenes is as optimization aim, when collaboration optimizes the planning and operation of different time scales
Uncertainty, establish active distribution system collaborative planning model；
S200, active distribution system collaborative planning model is solved using improved particle swarm optimization algorithm, and with economy
The maximum of property and renewable energy permeability turns to target, to obtain optimal solution.
2. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 1,
It is characterized in that, the active distribution system collaborative planning model established in the step S100 includes: the foundation of typical daily scene
And the ADS plan model based on multi-objective and multi-hierarchy optimization.
3. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 1,
It is characterized in that, active distribution system collaborative planning model is established in the step S100 and includes the following steps: collaborative planning model
Frame；Optimization object function；Operation constraint for equipment investment constraint, network and ESS proposes corresponding constraint condition.
4. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 1,
Be characterized in that, in the step S100 optimization aim by the preferred plan of multi-target method, make DGO net profit maximize with
And it obtains more incomes and constructs optimization object function, and the optimization aim includes: upper layer target, middle layer target and lower layer's mesh
Mark；
The upper layer target is to carry out planning network from the angle of DSO, is considered from two targets of economy and RES permeability,
To obtain preferred plan；
The building concrete mode of economy objectives:
Economic goal function is constructed by cost of investment, operation and maintenance cost, which can use formula (1)-(7)
It indicates:
In formula,Feeder line day fund cost is represented,For feeder line day operation maintenance cost,For scene
Network operation cost under sc, ηscFor scene sc probability of happening, NscFor scene sc number；
In formula, ΩRF,ΩAFReplacement feed line and additional feed line are respectively represented,Respectively with replacement feed line A and
The relevant variable of existing feed line B,For replace feed line A day capital cost,To add feed line B's
Day capital cost, δFFor feeder line rate of recovery of capital；
In formula, ΩEFFor existing feed line collection,For replace feed line A operation and maintenance cost,For additional feedback
The operation and maintenance cost of electric wire B,For variable relevant to existing feed line C,For existing feed line C day operation
Maintenance cost；
In formula, EPtFor tou power price t, EPPV,EPWGRespectively photovoltaic contract electricity price and wind-powered electricity generation contract electricity price,For scene sc
The active output of photovoltaic n when middle time t,For in scene sc when time t wind-powered electricity generation m active output；
In formula, ΩbusTo load bus group,For in scene sc, the burden with power demand of bus i when time t,
In scape sc, the active output of ESS k when time t,Active via net loss in scene sc, when time t；
In formula, Ia,sc,t,Ib,sc,tIc,sc,tIn respectively scene sc, the electric current of feeder line A/B/C, R when time ta,Rb,RcNot to be
The resistance value of feeder line A/B/C；
In formula,For controller switching equipment rate of recovery of capital, r is interest rate,For the controller switching equipment service life；
The building concrete mode of RES permeability target:
Alleviate network congestion by building RES permeability function optimization network structure, to improve the infiltration of RES to the maximum extent
Rate, the RES permeability function can be indicated with formula (8)-(10):
In formula, RESPscFor the RES permeability in scene sc,For the energy provided in scene sc by RES,For field
Workload demand energy in scape sc；
In formula,For in scene sc, the active output of photovoltaic n when time t,For in scene sc, wind-powered electricity generation m when time t
Active output；
In formula,For in scene sc, the burden with power demand of bus i when time t,In scene sc, having when time t
Function via net loss.
5. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 4,
It is characterized in that, the middle layer target:
The configuration for optimizing RES and ESS from the angle of DGO is optimized by constructing middle layer objective function, makes the net profit of DGO
Profit maximizes, which can be indicated with formula (8)-(10):
In formula,For the day net profit of DGO in scene sc；
In formula,The day net profit of PV n/WG m/ESS k in respectively scene sc；
In formula (13)-(15),The day benefit of PV n/WG m/ESS k in difference scene sc,
Respectively PV n/WG m days totle drilling cost,For the day totle drilling cost of ESS k in scene sc；
In formula (16)-(21), ccPV,ccWGThe unit capital cost (dollar/kw) of respectively PV/WG, ccPCS,ccB&RRespectively with
The relevant ESS unit capital cost of power conversion system/storage cavern, cmPV,cmWGRespectively PV/WG unit O&M cost, cmFOM,
cmVOMRespectively ESS unit fixes/variable operation maintenance cost, Pn PV,R,The respectively rated power of PV n/WG m；
Lower layer's target: be based on the considerations of typical scene to ESS carry out optimal scheduling, by building lower layer's objective function into
Row considers that, to obtain more incomes, which is formulated are as follows:
6. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 3,
It is characterized in that, the constraint condition includes:
Installed capacity limitation: in order to mitigate bi-directional current bring adverse effect, the installed capacity ratio of RES should be less than allowing
The installed capacity of value, the RES indicates:
In formula, λIAllow maximum value, S for RES permeabilitysubFor high, Zhong Ya electric substation rated power；
Network operation constraint: network operation constraint include active/reactive power equilibrium equation, power flow equation and electric current and
The security constraint of voltage, the network operation constraint representation:
In formula,In respectively scene sc when time t high pressure/middle buckling power station it is active/reactive power output, Pi,sc,t,
Qi,sc,tIn respectively scene sc in time t bus i active/reactive power, Ui,sc,t,θij,sc,tWhen in respectively scene sc
Between t when bus i and bus j between voltage amplitude/voltage angular difference, Gij,BijTransmission electricity between respectively bus I and bus J
/ susceptance is led,Respectively bus I voltage amplitude allowed band,Respectively feeder line A/B/C is most
High current；
The operation of ESS constrains, and the operation of ESS should be strictly by cyclic behaviour, state-of-charge and charge-discharge electric power allowed band
Limitation, the operation constraint representation of the ESS:
Socmin≤Sock,sc,t≤Socmax (32)
In formula,For the rated power of ESS k, Socmax,SocminFor state-of-charge allowed band,For scene sc
In, the energy being stored in time t in ESS k battery pack, ηC,ηDESS efficiency for charge-discharge respectively.
7. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 2,
It is characterized in that, detailed process is as follows for the foundation of the daily scene of typical case:
Firstly, the year prediction data of wind speed, solar irradiation and workload demand is unitized by respective maximum value and minimum value, so
Unitized year related data is divided into 365 day section afterwards, establishes initial day scene matrix；
Secondly, 365 day section is clustered into typical day section by K- mean cluster, in order to ensure selected typical case is daily
The quality and diversity of scene, number of clusters by Davies Bouldin Effective exponent IDBIt determines, later, by matrix
SinitialBe converted to typical daily scene matrix Sclustered, IDBIt indicates are as follows:
Rgh=(Sg+Sh)/dgh (35)
dgh=| | cg-ch|| (37)
In formula, IDBFor Cluster Validity index, NCFor k means Clustering number, NgFor cluster centre vector number g, dghFor cluster
The distance between center G and cluster centers H, Sg,ShRespectively cluster centers dispersion degree g/ cluster centers h, cg,chRespectively collect
Group center G/ cluster centers H；
Then variable after Matrix Cluster is restored to original boundary, be used for optimization planning scheme.
8. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 1,
It is characterized in that, improved particle swarm optimization algorithm includes following method in the step s200:
(1) Tent chaotic maps: in order to avoid pseudo-random number sequence, using ergodic and the better Tent chaotic maps of randomness
Generate initial population；
(2) it the combination of genetic algorithm and particle swarm algorithm: in modified particle swarm optiziation, introduces the quickly non-sequence that is dominant and loses
The quick non-sort method that is dominant used in propagation algorithm, meanwhile, it is distributed according to the crowding distance of non-domination solution, selects each iteration
Globally optimal solution；
(3) integer programming based on wheel disc algorithm: the integer programming in middle layer in consideration, position of each particle in solution space must
Integer variable must be converted to, integer programming is carried out using wheel disc algorithm.
9. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 1,
It is characterized in that, improved particle swarm optimization algorithm can use three when handling upper layer target, middle layer target and lower layer's target respectively
The different optimization process of kind, and the specific optimization process of improved particle swarm optimization algorithm is as follows:
Firstly, the multi-objective particle swarm algorithm based on Pareto, generates upper layer initial population using tent chaotic maps；
Then, middle layer is sent by the upper layer of particles for representing candidate network structure, at this time in candidate network structure, chaos grain
Swarm optimization produces middle layer initial population；
Later, the middle layer particle for representing RES and ESS candidate allocation scheme and specified network are sent collectively to lower layer, therefore,
Chaos particle swarm optimization algorithm can optimize the scheduling of each ESS under particular network condition, and the distribution to RES and ESS
It optimizes；
Then the traffic control of obtained each ESS is fed back to middle layer, calculates the optimization aim of the candidate allocation scheme,
Middle layer optimization after, by after optimization allocation plan and job scheduling feed back to upper layer, calculate the object of planning of DSO；
Identical step is finally repeated, until the multiple target PSO convergence based on Pareto.
10. renewable energy energy-storage system collaborative planning method in a kind of integrated energy system according to claim 2,
It is characterized in that, the ADS plan model based on multi-objective and multi-hierarchy optimization specifically includes: for optimizing network planning issue
Upper layer model, the middle layer model of assignment problem for solving RES and ESS and the ESS for optimizing each typical scene are transported
Row scheduling, feeds back to upper layer and middle layer, calculates the lower layer of the net profit of the ADS operation cost and DGO under different scenes per hour
Model.
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Application publication date: 20191108 |