CN110163450A - A kind of distribution network planning bi-level optimal model construction method limited based on operation - Google Patents
A kind of distribution network planning bi-level optimal model construction method limited based on operation Download PDFInfo
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Abstract
The invention discloses a kind of distribution network planning bi-level optimal model construction methods limited based on operation, the following steps are included: investing to build time and addressing constant volume as decision variable using route, energy storage, upper layer investment construction Optimized model and lower layer's management and running Optimized model are constructed；Using Lagrange multiplier will constraint condition be added objective function in, make it is original be changed into unconstrained optimization problem containing equation and the optimization problem of inequality constraints, utilize the solution of the final problem of implementation of Kuhn-Tucker condition；Screen limited Run-time scenario based on shadow price, the shadow price indicate to assume 1 unit of the resource capacity expansion reduction scheduling cost；Decoupling processing is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model.
Description
Technical field
The present invention relates to power distribution network optimisation technique field, especially a kind of distribution network planning limited based on operation is double-deck
Optimized model construction method.
Background technique
Distribution network planning target be on the basis of meeting load development and other requirements such as long-term safety stable operation,
It pursues economic benefit and social benefit is optimal.Nowadays, many distribution network planning models use operation-planning bilayer model, full
Under the premise of sufficient operational reliability constraint, to plan totle drilling cost as objective function, the smallest programme of totle drilling cost is obtained.It is existing
Consider although the operation problem under the more scenes of power distribution network is included in plan model by some Bi-level Programming Models, in the structure of model
It builds and solves aspect and still have shortcoming, such as do not account for extreme scenes, relevant feedback variable is thin and it is multiple to solve
Miscellaneous degree is higher.
Summary of the invention
The object of the present invention is to provide a kind of distribution network planning bi-level optimal model construction method limited based on operation,
, solution steady performance high with solution efficiency.
To achieve the above object, the present invention adopts the following technical solutions:
A kind of distribution network planning bi-level optimal model construction method limited based on operation, comprising the following steps:
Invest to build time and addressing constant volume as decision variable using route, energy storage, construct upper layer investment construction Optimized model and
Lower layer's management and running Optimized model；The planning layer objective function of the building upper layer investment construction Optimized model is comprising investment
The total annual cost of cost, Environmental costs and limitation scene operating cost；The firing floor mesh of the building management and running Optimized model
Scalar functions are each scene daily economic dispatch cost；
Using Lagrange multiplier constraint condition is added in objective function, is made original containing equation and inequality constraints
Optimization problem is changed into unconstrained optimization problem, utilizes the solution of the final problem of implementation of Kuhn-Tucker condition；
Limited Run-time scenario is screened based on shadow price, the shadow price indicates to assume 1 unit institute of the resource capacity expansion
The scheduling cost of reduction；
Decoupling processing is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model.
Further, the building upper layer investment planning is built Optimized model and is invested pair using route and energy storage as planning
As, it is optimized for annual total cost minimum, takes into account the cost of investment, limitation scene operating cost and Environmental costs of system,
Objective function such as following formula:
minF(xes,Pnes,Enes,xl,xl')=min Cinv+Com
Wherein, CinvFor the equal years value expense of cost of investment conversion, A=(1+ ε)-iFor recovery of the capital coefficient, ε is to discount
Rate, ΩESS,ΩNESS,ΩLine,ΩNLineFor energy storage to be expanded and newly-built, line set.cs,cP,cEFor energy storage infrastructure
Cost, unit power cost and unit capacity cost, cl,cl' it is newly-built and route unit length to be expanded maintenance cost.
Further, the constraint condition of the building upper layer investment planning construction Optimized model includes:
Decide whether it is newly-built or be transformed route, whether the type selecting of newly-built or dilatation and energy storage dilatation capacity and electricity
It is constrained with construction；Using hour as thread, and use DC flow model ignores the power distribution network of voltage landing between each node
The constraint of typical scene reliability service；Meet the grid structure constraint of connectivity constraint and radial constraint.
Further, it is described building lower layer's management and running Optimized model power distribution network day economic traffic control cost by DG
Normal power supply cost, via net loss cost, the cost of electric energy of trading, energy storage " low Chu Gaofang " and abandonment, abandoning light, cutting load etc.
The punishment cost of unconventional scheduling means is constituted, objective function such as following formula:
In formula, s, i, j, t respectively indicate scene number, node serial number and scheduling slot number, and Δ t is each scheduling phase
Duration is believed that each distributed generation unit power output, energy-storage units power output and payload are constant to each stage；
ΩG、ΩLine、ΩWF、ΩPV、Ωgrid、ΩLoad、ΩBESSRespectively distribution system conventional thermal power unit node set, sets of lines
Conjunction, blower economize on electricity, photovoltaic node, connection major network node, load bus and energy storage node set, cWF,cPV,cLoss,cSell,
cPur,cWFP,cPVP,cENSRespectively wind power supply, photovoltaic power supply, network loss, the list for major network purchase sale of electricity, abandonment, abandoning light, losing load
Position cost or unit punishment cost；Wherein, cPurUsing tou power price,Table respectively
Show scene power output, major network purchase electricity sales amount, cutting load amount and discharge and recharge, is the partial decision variable of firing floor.
Further, the constraint condition of building lower layer's management and running Optimized model includes:
The supply side constraint of joint constraint is constrained and balanced comprising distributed generation resource；Line Flow constraint and network security
Operation constraint；The model of controllable burden constrains；The model of energy-storage system constrains.
Further, described that limited Run-time scenario is screened based on shadow price, it specifically includes:
The each Lagrange multiplier of limited Run-time scenario is modified, uniform units and magnitude, establishes total operation limit
Performance indicator；
Comprehensively consider scene probability of happening and limited severity, obtain impact factor；
The scene that limited most serious is run to the screening of Run-time scenario influence degree, by network solution scheme and non-network solution side
Case iteration forms programme, filters out NS limitation scene of most serious.
Further, described that upper layer investment construction Optimized model and lower layer's management and running Optimized model are carried out at decoupling
Reason, specifically includes:
Distribution network planning-operation Decoupled Model is proposed, to the scene in intermediate fuzzy area, objective function, constraint item
Part carries out processing decoupling:
In formula, F (x, y) and f (x, y) are respectively planning layer and firing floor objective function, and H (x) and h (x, y) are inequality
Constraint, g (x, y) are equality constraint, and x is planning layer decision variable, ysFor the firing floor decision variable under scene s, " * " number table
Show that the decision variable is in limitation scene.
The effect provided in summary of the invention is only the effect of embodiment, rather than invents all whole effects, above-mentioned
A technical solution in technical solution have the following advantages that or the utility model has the advantages that
The present invention invests to build time and addressing constant volume as decision variable using route and energy storage, and it is excellent to establish planning-operation bilayer
Change model.Optimized results are transferred to lower layer by upper layer, influence planning layer constraint condition.It is double that the present invention calculates distribution network planning
Layer Lagrange multiplier, proposes the limited Run-time scenario screening strategy based on shadow price, proposes distribution network planning bilayer model
Decoupling method, and using the modeling tool Yalmip based on MATLAB and CPLEX solver is called to solve this model.
The mentioned method of the present invention has solution efficiency height, solves steady performance.
Detailed description of the invention
Fig. 1 is flow chart of the embodiment of the present invention；
Fig. 2 is the distribution network planning bilayer model schematic diagram of building of the embodiment of the present invention；
Fig. 3 is to carry out solution flow chart to the present embodiment building model.
Specific embodiment
In order to clarify the technical characteristics of the invention, below by specific embodiment, and its attached drawing is combined, to this
Invention is described in detail.Following disclosure provides many different embodiments or example is used to realize difference of the invention
Structure.In order to simplify disclosure of the invention, hereinafter the component of specific examples and setting are described.In addition, the present invention can
With repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not
It indicates that the relationship between various embodiments and/or setting is discussed.It should be noted that illustrated component is not necessarily in the accompanying drawings
It is drawn to scale.Present invention omits the descriptions to known assemblies and treatment technology and process to avoid this is unnecessarily limiting
Invention.
As shown in Figure 1, the distribution network planning bi-level optimal model construction method limited based on operation, comprising the following steps:
S1, time and addressing constant volume are invested to build as decision variable using route, energy storage, building upper layer investment construction optimizes mould
Type and lower layer's management and running Optimized model.
S2, constraint condition is added in objective function using Lagrange multiplier, is made original containing equation and inequality constraints
Optimization problem be changed into unconstrained optimization problem, utilize the solution of the final problem of implementation of Kuhn-Tucker condition.
S3, limited Run-time scenario is screened based on shadow price.Shadow price indicates to assume 1 unit institute of the resource capacity expansion
The scheduling cost of reduction.
S4, decoupling processing is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model.
The present embodiment invests to build time and addressing constant volume as decision variable using route and energy storage, and it is double-deck to establish planning-operation
Optimized model.Planning layer objective function is the total annual cost comprising cost of investment, Environmental costs and limitation scene operating cost.
Optimized results are transferred to lower layer by upper layer, influence planning layer constraint condition.Firing floor objective function is each scene day economy tune
Cost is spent, the distribution network planning bilayer model that meter and operation are limited is as shown in Figure 2.
In step S1, time and addressing constant volume are invested to build as decision variable using route, energy storage, construct upper layer investment construction
Optimized model and lower layer's management and running Optimized model, specifically include:
S11, building upper layer investment construction Optimized model:
Upper layer investment construction Optimized model is planned using route and energy storage as planning investee, for annual total cost
Minimum optimizes, it is contemplated that cost of investment, limitation scene operating cost and the Environmental costs of system, objective function such as formula (1)
It is shown.
minF(xes,Pnes,Enes,xl,xl')=min Cinv+Com
Wherein, CinvFor the equal years value expense of cost of investment conversion, A=(1+ ε)-iFor recovery of the capital coefficient, ε is to discount
Rate.ΩESS,ΩNESS,ΩLine,ΩNLineFor energy storage to be expanded and newly-built, line set.cs,cP,cEFor energy storage infrastructure
Cost, unit power cost and unit capacity cost.cl,cl'For newly-built and route unit length to be expanded maintenance cost.
Layer is invested generally from investment construction angle, capacity limit is carried out to the ESS that can be built, while generally needing
Consider that the state of built route and route yet to be built and the maximum number of, lines that every route can configure are set, considers
I.e. all one radial power distribution networks of railway superstructures are limited to grid structure.Therefore, the constraint condition for investing layer is main
Include:
(1) type selecting and construction constrain:
1) whether create or be transformed route and whether newly-built or dilatation energy storage is 0-1 variable, need to meet:
Wherein, xness,y,xcness,y,xnline,y,xcline,yRespectively energy storage is newly-built, dilatation and route are newly-built, upgrading decision
Variable.
2) capacity Yu electricity of energy storage dilatation should meet certain technical conditions:
Wherein,WithThe respectively maximum capacity and electricity that can configure of energy storage.
(2) distribution typical scene reliability service constrains:
In the planning layer constraint for considering typical scene reliability service, since planning layer is to the precision of operation calculated result
It is required that it is lower, difficulty is solved to reduce, improves computational efficiency, this planning uses DC power flow using hour as thread
Model has ignored the voltage landing between each node, and operation constraint is expressed as follows with formula:
1) distributed generation resource constrains
In formula, θWF, θPVRespectively wind-power electricity generation and photovoltaic power generation abandonment, abandoning light rate,It indicates under scene s
The highest of wind-powered electricity generation and photovoltaic power output.
2) balance nodes constrain
In formula,Indicate that balance nodes are active and reactive power,Respectively balance nodes maximum, minimum have
Function power.
3) trend constraint
In formula,Pline, PD, PENS, PPur, PSell,Conventional power source is respectively indicated to go out
Power, line power, load power, cutting load amount, from major network purchase sale of electricity power, scene power output, cut discharge and recharge.
The charge and discharge 0-1 quantity of state of ESS on respectively t period node j.
4) controllable burden maximum cutting load constrains
The scarce power supply volume (Energynot Supply, ENS) of controllable burden is limited by the contract of controllable burden.
In formula, λ indicates controllable burden node maximum cutting load rate,It is born for the active of t period interior joint j under scene s
Lotus.
5) energy-storage system power limit
6) energy storage system capacity limits
In formula,Indicate that the remaining capacity of energy storage is horizontal, EESSFor the capacity of ESS, Pes,c、Pes,disFor the charge and discharge of ESS
Electrical power, ηes,c、ηes,disThe respectively efficiency for charge-discharge of ESS.It, will for the service life for guaranteeing energy storageIt is limited in maximum
The 20%~90% of electric energy storage capacity,Indicate the initial value of electricity,Indicate the maximum value of electricity.
To simplify the calculation, it reduces and solves difficulty, rationally ignore the self-discharge rate ε of energy storage in planning layer.In addition, according to each
The net load degree of node pre-processes the charging and discharging state of energy storage, cuts down 0-1 variable.
(3) grid structure constrains
The grid structure of power distribution network need to meet connectivity constraint and radial constraint.Connectivity constraint requires power distribution network each
Node is connected with nodes other in network.Distribution network closed-loop design, open loop operation, radiativity constraint requirements power distribution network do not go out
The case where existing loop-net operation.
1) branch that each load bus power flows into has and only one, the branch of each substation's node power outflow
At least one, road, as shown in formula 10,11.
When i is load bus:
When i is power transformation tiny node:
In formula, αij, βijFor 0-1 variable, the power flow direction of route, α are indicatedijIndicate line power from i-node stream when taking 1
To j node, βijIndicate that route is not built when indicating that line power flows to i-node from j node when taking 1, while taking 0.
2) power distribution network network is tree, and it is total connectivity constraint can be expressed as the network line with the method for graph theory
Number is equal to node total number and subtracts 1, it may be assumed that
S12, building lower layer's management and running Optimized model:
Power distribution network day, economic traffic control cost was by DG normal power supply cost, via net loss cost, the cost of electric energy of trading,
Energy storage " low Chu Gaofang " and abandonment, the punishment cost composition for abandoning the unconventional scheduling means such as light, cutting load, objective function is such as
Shown in formula 4.In actual operation, power distribution network is not intended to that there is a situation where the amounts of falling power transmission to major network, therefore sets selling to major network
Electric cost is lower than purchases strategies.It is that renewable energy and energy storage are filled that the objective function, which inherently can reflect Optimized Operation,
Electric discharge strategy rationalizes bring income, makes full use of distributed energy, rational management energy storage can make operating cost more
It is small.
In formula, s, i, j, t respectively indicate scene number, node serial number and scheduling slot number, and Δ t is each scheduling phase
Duration is believed that each distributed generation unit power output, energy-storage units power output and payload are constant to each stage.
ΩG、ΩLine、ΩWF、ΩPV、Ωgrid、ΩLoad、ΩBESSRespectively distribution system conventional thermal power unit node set, sets of lines
Conjunction, blower economize on electricity, photovoltaic node, connection major network node, load bus and energy storage node set, cWF,cPV,cLoss,cSell,
cPur,cWFP,cPVP,cENSRespectively wind power supply, photovoltaic power supply, network loss, the list for major network purchase sale of electricity, abandonment, abandoning light, losing load
Position cost or unit punishment cost.Wherein cPurUsing tou power price,Table respectively
Show scene power output, major network purchase electricity sales amount, cutting load amount and discharge and recharge, is the partial decision variable of firing floor.Firing floor is determined
Plan variable further includes the charging and discharging state etc. of day part energy storage.
The daily economic dispatch model of active distribution network considers the constraint of " source, net, lotus, storage " four aspects:
(1) supply side constrains
1) distributed generation resource constrains
Distributed generation resource highest power output determined by the wind speed and intensity of illumination of Run-time scenario, minimum output by maximum abandonment,
The limitation for abandoning light rate, sets distributed electrical source operating mode as constant dc power control herein.
2) balance nodes constrain
It herein using the substation between power distribution network and higher level's power grid as balance nodes, and is distribution by higher level's power grid
Net provides spare.When considering real system operation, major network formulates production plan according to load prediction information a few days ago, and guarantees
Certain spinning reserve, it is therefore desirable to consider balance nodes adjustable range.
In formula,Indicate that balance nodes are active and reactive power,It is respectively flat
Weigh node maximum, minimum active and reactive power.
(2) Line Flow constraint is constrained with Cybersecurity Operation
1) trend constraint
For radial distribution system, Branch Power Flow model is as follows:
In formula, rij, xijThe respectively resistance of route ij and reactance, ViFor the voltage magnitude of AC distribution net node i.
2) power distribution network safe operation constraint
It is of less demanding to the solving precision of traffic control model when due to solving planning problem, linearisation can be used
DistFlow Branch Power Flow model is substituted, and formula (17), (18), (19) can convert are as follows:
The current-carrying capacity constraint that formula (17) indicates mathematically indicates a round inside, can use one round inscribed positive ten
Two side shapes carry out this circle of approximate representation, so that the nonlinear restriction represented by it can be substituted by following formula:
After above-mentioned simplification and deformation, the MINLP model of former problem is changed into MILP model, and it is excellent that mathematics can be used
The ripe algorithm changed in software quickly acquires globally optimal solution.
(3) the model constraint of controllable burden
The scarce power supply volume (Energy not Supply, ENS) of controllable burden is limited by the contract of controllable burden.
In formula, λ indicates controllable burden node maximum cutting load rate,It is born for the active of t period interior joint j under scene s
Lotus.
(4) the model constraint of energy-storage system
1) charging and discharging state limits
2) charge and discharge number limits
Wherein,Indicate the maximum value of charge and discharge number.Formula is non-linear, is realized and is linearized by following formula.
3) power limit
4) capacity limit
In formula, to improve firing floor computational accuracy, ε, which is added, indicates the self-discharge rate of unit time period energy storage.
5) the charge and discharge conservation of energy constrains in diurnal periodicity
In formula, α and β are respectively to be charged and discharged efficiency.
In step S2, distribution network planning problem is one non-linear excellent comprising equation and inequality constraints condition simultaneously
Change problem gives objective function according to planning requirement, its most value in constraint condition is asked (generally to ask programmed cost minimum
Value).In order to solve the optimization problem, using Lagrange multiplier will constraint condition be added objective function in, make it is original containing etc.
Formula and the optimization problem of inequality constraints are changed into unconstrained optimization problem, utilize Ku En-Plutarch (Kuhn-Tucker) condition
The solution of final problem of implementation.
Assuming that there is optimization problem as shown in formula 32,33,34.
Min c(x) (32)
s.t. h(x)≤b (33)
G (x)=d (34)
Wherein, h (x)=(h1(x),h2(x),...,hm(x))T, it is the set of m inequality constraints；G (x)=(g1
(x),h2(x),...,gn(x))T, it is the set of n equality constraint.
Lagrangian such as formula 35 can be established:
Γ=c (x)+μT[h(x)-b]+λT[g(x)-d] (35)
Wherein, μ and λ is respectively the Lagrange multiplier of inequality and equation, and obtains optimality condition (Ku En-Plutarch
Condition) as shown in Equation 36.
Wherein, μT[h (x)-b]=0 is to benefit relaxation condition, it is desirable that one in μ or h (x)-b is necessary for zero.
In step S3, screening limited Run-time scenario based on shadow price includes:
The selection of S31, operation limitation scene and its index
The operation that each Run-time scenario includes constrains more than one, therefore single game scape contains multiple Lagrange multipliers.For
Unified evaluation is carried out to the limited degree of each scene, total operation marginal benefit index of each item constraint need to be established, consider rule
When drawing layer model, screening is ranked up to the index, critical constraints scene in the top is obtained, in objective function and constraint
It is embodied in condition, realizes the decoupling of operation-planning problem.
There is diversified constraint condition in single Run-time scenario, as capacity of trunk constraint, abandonment abandon light constraint,
Controllable burden maximum cutting load amount and energy-storage system charge-discharge electric power and capacity-constrained etc..It is different to constrain classification, the order of magnitude and
Unit is different.For example, the corresponding Lagrange multiplier unit of the capacity-constrained of certain energy storage is member/kWh, and its charge and discharge power is about
The corresponding Lagrange multiplier unit of beam is member/kW, respectively indicates to energy storage amplification unit capacity and increases unit power institute
The marginal benefit of operation can be generated.Each Lagrange multiplier need to be modified to limited, uniform units and magnitude are established
Total operation marginal benefit index, as shown in Equation 37.
In formula, μs,st,tAnd ζs,st,tForward and backward Lagrange multiplier, c are corrected for Run-time scenario s dispatching period tstFor
Modifying factor.
If total operation marginal benefit of a certain scene is very big, limited degree is serious, but its probability of happening is extremely low, is individually for
This limitation scene increases investment, and the economic benefit is not high, it may be considered that is ignored.If the probability of happening of a certain scene it is larger and
Limited degree is low, for example from the day-to-day operation scene of limit, considers without in planning layer.In planning problem, for reality
Existing returns of investment maximize, should comprehensively consider scene probability of happening and limited severity, obtain impact factor such as formula 38
It is shown.
In formula, psFor the probability of happening of scene s, ΩstFor the relevant constraint set of plan objects, ψsIt indicates in scene collection s
The sum of the modified Lagrange multiplier of each scheduling slot constraint relevant to plan objects indicates planning layer specific investment cost institute energy
The marginal benefit of generation.
S32, limited Run-time scenario screening model is determined
Power distribution network is limited Run-time scenario and refers under current electric network state, and the derivative risk of the scene operational decisions is larger, economical
Property less reliable, should highly alleviate risk in the following distribution network planning, improve the field of its performance driving economy and reliability
Scape.Stringent screening magnanimity Run-time scenario, is the basis for planning scene setting.
In the case where n-th order section is with net state, whether Run-time scenario s should consider in planning, under the scene operating condition
Investment-income of limited resources is more related than with the probability of occurrence ps, n of the scene.Operation is limited by Internet resources, and scene occurs
Probability it is excessively high or it is caused run limited cost it is excessive when, should usually use Networking Solutions & provisioned.Run-time scenario s is defined to more
The influence degree A of stage planningsAre as follows:
In formula: ψs,nIt is the variable measuring the Internet resources of n-th order section and being influenced on scene s operating cost.
The shadow price of Internet resources reflects influence of the network capacity extension to operation in running optimizatin scheduling model.According to
The definition of shadow price in the principle of optimality, considers in the economic load dispatching model of network constraint, corresponding network resource (such as route)
Constraint condition in action when, shadow price indicate assume 1 unit of the resource capacity expansion (such as route dilatation 1MW), institute
The scheduling cost of reduction.
Open space planning of the invention includes that route expansion scheme and energy storage allocation plan two parts form.Route, energy storage etc.
The corresponding cost of investment of the unit capacity of different projected resources is different, therefore copes with shadow price and be modified, with the network capacity extension
Specific investment cost expense generate scene s operating cost marginal benefit as ψs,n。
In traditional distribution planning, usually to envision scene operating cost and network capacity extension cost of investment minimum as rule
The objective function for the problem of drawing solves certainty programme.Then consider the feelings of deviation occur in reality scene and anticipation scene
Under condition, the adaptability of programme is analyzed, carries out the ratio choosing of distribution programme.Invention emulates the industry of traditional planning
Be engaged in process, run the scene of limited most serious to the screening of Run-time scenario influence degree by planning, by network solution scheme with it is non-network
Solution scheme iteration, gradually forms programme.
In the α times iteration, it is limited Run-time scenario screening model are as follows:
In formula:For the complete or collected works of Run-time scenario,For the limited fortune under current Internet resources, considered needed for future plan
Row scene collection.The ratio that middle scene operating cost is accounted for all Run-time scenarios by Internet resources effect is δ, optimization
Purpose is the N for filtering out most seriousSA limitation scene considers in planning.
S33, shadow price method for solving is proposed
In view of the active management ability of ADN operation, in Run-time scenario s, power distribution network passes through the coordination control that source lotus net stores up
System, active management meet service condition, realize that running optimizatin dispatches cost minimization.Therefore, ψsMeaning it is to be understood that first wife
The numerical value reduction amount of the operating cost minimum value of bring Run-time scenario s when electric network Expansion Planning consumes unit cost.
Under power distribution network scene s, to dispatch costThe Optimized model of minimum target are as follows:
In formula: ys,nFor operational decisions variable, such as DG power generation, energy storage charge and discharge state and electricity, cutting load amount etc.； hs,n
=0 indicates equality constraint；gs,n≤ 0 indicates inequality constraints；xnIndicate the decision variable that the planning of n-th order section is invested to build.
With ΩrIndicate the resource collection that can be created or extend in planning.According to the principle of optimality, if under Run-time scenario s, net
Network resource r (r ∈ Ωr) the corresponding shadow price μ of constraint conditions,n,r> 0, then resource r has been fully used, without nargin,
If increasing by 1 unit capacity, system operation cost reduces μs,n,r.The different types resource unit costs such as distribution line, energy storage
Difference, the unit cost c of Internet resources rn,r, the marginal benefit of the specific investment cost expense of the network capacity extension to scene s operating cost
ψs,n(abbreviation dilatation marginal benefit) is defined as:
The scheduling means such as non-network solution, including abandonment/light, cutting load of power distribution network, operating costIncluding
The use cost or punishment cost of these means:
In formula: the cycle of operation includes T scheduling slot, when each scheduling slot a length of Δ t；T is scheduling slot number；j
For node serial number；Ωgr, ΩL, ΩWF, ΩPVRespectively connect major network node, load bus and blower, photovoltaic node set；cENS, cWFP, cPVPTou power price and cutting load, abandonment, the unit cost for abandoning light are respectively indicated, i.e.,Including master
Light cost is abandoned in net purchases strategies, cutting load cost and abandonment；When indicating that the wind under scene s, light generate electricity each
The full sending power of section； Purchase of electricity, cutting load amount and blower, photovoltaic power output are respectively indicated,
It is the decision variable of firing floor.The objective function indicates that firing floor makes full use of DG, rational management ESS charge and discharge, can drop
The operating cost of low sub-scene.
The equality constraint h of scene s operations,n(ys,n)=0 includes there is (no) function Constraints of Equilibrium, energy storage remaining capacity timing
Constraint and discharge and recharge conservation constraints:
Wherein:
In formula: i, k are node serial number；ΩbFor distribution line set；rij, xijIndicate line resistance and reactance；Vi,t,s,nTable
Show node i voltage magnitude；Pj,t,s,n, Qj,t,s,nIndicate the active and reactive power of node injection；Indicate line
The effective power flow and reactive power flow on road；Indicate that the remaining capacity of energy storage is horizontal；ε is the self discharge of unit period energy storage
Rate；ηCh, ηDisRespectively energy storage efficiency for charge-discharge.
The inequality constraints g of Run-time scenario Optimal Operation Models,n(xn,ys,n)≤0 include blower/photovoltaic power output, cut it is negative
Lotus amount, energy storage charge/discharge power, energy storage charge state and capacity of trunk constraint, it may be assumed that
In formula: θWF, θPVIt respectively indicates the maximum abandonment of distribution system permission, abandon light rate；Indicate maximum cutting load rate；For minimum, maximum state-of-charge rate；Reserve of electricity is generally in the 20%~90% of its capacity limit[4]；It is the active and reactive load of node；For 0-1 variable, the available shape of route and energy storage is respectively indicated
State；Respectively energy storage charging and discharging state；Pj,n,maxFor the power maximum value of ESS；Ej,n,maxFor the electricity of ESS
Capacity.SijIndicate the maximum power supply capacity of route.
The inequality constraints condition g of Run-time scenario Optimal Operation Models,n(xn,ys,n)≤0 further includes network voltage constraint,
That is:
In formula: H is that (as newly-built branch ij, formula is consistent with common distribution trend for sufficiently large real number；When not building branch
When the ij of road, H makes trend constraint at this branch fail, i.e. Vi,t,s,nWith Vj,t,s,nThere is no connections)； Vj,min,Vj,maxMost for node
Small, maximum voltage.
In step S4, decoupling processing tool is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model
Body includes:
Distribution network planning-operation Decoupled Model is now proposed, to the scene in intermediate fuzzy area, objective function, constraint
Condition carries out processing decoupling, as shown in Equation 37.
In formula, F (x, y) and f (x, y) are respectively planning layer and firing floor objective function, and H (x) and h (x, y) are inequality
Constraint, g (x, y) are equality constraint.X is planning layer decision variable, ysFor the firing floor decision variable under scene s, " * " number table
Show that the decision variable is in limitation scene.
Using the modeling tool Yalmip based on MATLAB and CPLEX solver is called to solve this model.Specifically
Process is as shown in Figure 3.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects to the present invention
The limitation of range, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art
Member does not need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (7)
1. a kind of distribution network planning bi-level optimal model construction method limited based on operation, characterized in that the following steps are included:
Time and addressing constant volume are invested to build as decision variable using route, energy storage, construct upper layer investment construction Optimized model and lower layer
Management and running Optimized model；It is described building upper layer investment construction Optimized model planning layer objective function be comprising cost of investment,
The total annual cost of Environmental costs and limitation scene operating cost；The firing floor objective function of the building management and running Optimized model
For each scene daily economic dispatch cost；
Constraint condition is added in objective function using Lagrange multiplier, asks original optimization containing equation and inequality constraints
Topic is changed into unconstrained optimization problem, utilizes the solution of the final problem of implementation of Kuhn-Tucker condition；
Limited Run-time scenario is screened based on shadow price, the shadow price indicates to assume that 1 unit of the resource capacity expansion is reduced
Scheduling cost；
Decoupling processing is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model.
2. the distribution network planning bi-level optimal model construction method limited based on operation as described in claim 1, characterized in that
The building upper layer investment planning builds Optimized model using route and energy storage as planning investee, most for annual total cost
It is small to optimize, the cost of investment, limitation scene operating cost and Environmental costs of system are taken into account, objective function such as following formula:
min F(xes,Pnes,Enes,xl,xl')=min Cinv+Com
Wherein, CinvFor the equal years value expense of cost of investment conversion, A=(1+ ε)-iFor recovery of the capital coefficient, ε is discount rate,
ΩESS,ΩNESS,ΩLine,ΩNLineFor energy storage to be expanded and newly-built, line set.cs,cP,cEFor energy storage infrastructure at
Originally, unit power cost and unit capacity cost, cl,cl'For newly-built and route unit length to be expanded maintenance cost.
3. the distribution network planning bi-level optimal model construction method limited based on operation as claimed in claim 2, characterized in that
It is described building upper layer investment planning construction Optimized model constraint condition include:
Decide whether it is newly-built or be transformed route, whether the type selecting and construction of newly-built or dilatation and energy storage dilatation capacity and electricity
Constraint；Using hour as thread, and use DC flow model ignores the power distribution network typical field of voltage landing between each node
The constraint of scape reliability service；Meet the grid structure constraint of connectivity constraint and radial constraint.
4. the distribution network planning bi-level optimal model construction method limited based on operation as described in claim 1, characterized in that
Building lower layer's management and running Optimized model power distribution network day economic traffic control cost by DG normal power supply cost, network damage
Cost is consumed, the cost of electric energy of trading, the punishment of the unconventional scheduling means such as energy storage " low Chu Gaofang " and abandonment, abandoning light, cutting load
Cost structure, objective function such as following formula:
In formula, s, i, j, t respectively indicate scene number, node serial number and scheduling slot number, and Δ t is each scheduling phase duration,
Each distributed generation unit power output, energy-storage units power output and payload are constant to be believed that each stage；ΩG、ΩLine、
ΩWF、ΩPV、Ωgrid、ΩLoad、ΩBESSRespectively distribution system conventional thermal power unit node set, line set, blower section
Electricity, photovoltaic node, connection major network node, load bus and energy storage node set, cWF,cPV,cLoss,cSell,cPur,cWFP,cPVP,
cENSRespectively wind power supply, photovoltaic power supply, network loss, major network purchase sale of electricity, abandonment, abandoning light, the unit cost of mistake load or unit are punished
Penalize cost；Wherein, cPurUsing tou power price,Respectively indicate scene power output, major network
Electricity sales amount, cutting load amount and discharge and recharge are purchased, is the partial decision variable of firing floor.
5. the distribution network planning bi-level optimal model construction method limited based on operation as claimed in claim 4, characterized in that
It is described building lower layer's management and running Optimized model constraint condition include:
The supply side constraint of joint constraint is constrained and balanced comprising distributed generation resource；Line Flow constraint and Cybersecurity Operation are about
Beam；The model of controllable burden constrains；The model of energy-storage system constrains.
6. the distribution network planning bi-level optimal model construction method limited based on operation as described in claim 1, characterized in that
It is described that limited Run-time scenario is screened based on shadow price, it specifically includes:
The each Lagrange multiplier of limited Run-time scenario is modified, uniform units and magnitude, establishes total operation marginal benefit
Index；
Comprehensively consider scene probability of happening and limited severity, obtain impact factor；
The scene that limited most serious is run to the screening of Run-time scenario influence degree, by network solution scheme and non-network solution scheme iteration
Programme is formed, NS limitation scene of most serious is filtered out.
7. the distribution network planning bi-level optimal model construction method limited based on operation as described in claim 1, characterized in that
It is described that decoupling processing is carried out to upper layer investment construction Optimized model and lower layer's management and running Optimized model, it specifically includes:
It is proposed distribution network planning-operation Decoupled Model, in intermediate fuzzy area scene, objective function, constraint condition into
Row processing decoupling:
In formula, F (x, y) and f (x, y) are respectively planning layer and firing floor objective function, and H (x) and h (x, y) are inequality constraints,
G (x, y) is equality constraint, and x is planning layer decision variable, ysFor the firing floor decision variable under scene s, " * " number is indicated should be certainly
Plan variable is in limitation scene.
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