CN110443467A - A kind of regional complex energy resource system solar energy digestion capability appraisal procedure - Google Patents
A kind of regional complex energy resource system solar energy digestion capability appraisal procedure Download PDFInfo
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Abstract
Facing area integrated energy system of the present invention, the method for proposing meter and energy network and the comprehensive digestion capability assessment of the probabilistic solar photoelectric of intensity of illumination-photo-thermal, first, determine regional power system parameter, region therrmodynamic system parameter, regional complex energy resource system device parameter and intensity of illumination predicted value and fluctuation range；Then set up two stages robust Model；Finally, being solved using two stages robust Model of the C&CG algorithm to building；The present invention can fully consider the influence of different type equipment and intensity of illumination uncertainty to regional complex energy resource system solar energy digestion capability.
Description
Technical field
The invention belongs to integrated energy system technical fields, are considering renewable energy stochastic volatility and comprehensive energy system
Unite synthetic operation on the basis of, be specifically designed it is a kind of based on robust optimization regional complex energy resource system solar energy digestion capability comment
Estimate method.
Background technique
The energy is economy, the important foundation of social development.With economy and society fast development, coal, petroleum, natural gas
Equal traditional energies consumption increasingly increases, and the energy and environmental crisis are increasingly sharpened, and Devoting Major Efforts To Developing is had become using renewable energy
Solve the contradictory inevitable choice of China's energy supply and demand.But there is randomness and interval by the renewable energy of representative of solar energy
Property the features such as, wide scale security consumption faces huge challenge.
The synthetic operation between the different energy sources system such as electric-thermal may be implemented in regional complex energy resource system, is that raising is renewable
The effective way of energy consumption level.Regional complex energy resource system not only can be used photovoltaic power generation technology and be dissolved in the form of electric energy
Solar energy can also use simultaneously the photothermal techniques such as solar energy heating to dissolve solar energy in the form of thermal energy, to give full play to difference
The consumption potentiality of energy network, the solar energy of lifting system entirety dissolve horizontal.But in view of not knowing for Intensity of the sunlight
Property and different energy sources system between reciprocal effect, how reasonable assessment area integrated energy system solar energy digestion capability, protect
The maximizing safety consumption of card solar energy has important practical significance.
Summary of the invention
The purpose of the present invention is design a kind of regional complex energy resource system solar energy digestion capability appraisal procedure.
For this purpose, technical solution of the present invention is as follows:
A kind of regional complex energy resource system solar energy digestion capability appraisal procedure, including the following steps carried out in order:
Step 1: it establishes regional complex energy resource system powering device model: establishing CHP unit model, gas fired-boiler respectively
Model, photovoltaic and heat collector power output uncertainty models, energy storage device model；
Step 2: regional complex energy resource system energy supply network model is established: using Distflow second order Based On The Conic Model to distribution
Net is modeled, and is modeled using linear heat supply network energy flow model to therrmodynamic system；
Step 3: corresponding model obtains energy network parameter, optional equipment parameter, electric heating from step 1 and step 2
Load curve parameter, the fluctuation range of the prediction Value Data and intensity of illumination of intensity of illumination；
Step 4: regional complex energy resource system solar energy digestion capability two stages robust assessment models: outer layer model are established
Objective function be solar energy equipment installed capacity it is maximum；The objective function of interior layer model is that grid net loss is minimum；
Step 5: it is solved using robust assessment models of the C&CG algorithm to building.
In step 4, solar energy digestion capability two stages robust assessment models objective function is as follows:
In formula, τ indicates the value coefficient of thermal energy,TaIndicate environment temperature, TsIndicate supply water temperature；ω is
The weight coefficient of grid net loss.PlossThe total power grid damage of expression system；I indicates the place of installation solar energy equipment；X, y are respectively
The decision variable in one stage and second stage；After Ω (x, u) is one group (x, u) given, the feasible zone of y；
To objective function compact form:
Constraint condition has compact form below:
In formula, Dy >=d indicates constraint only comprising second stage variable；H (x) y=u indicates the pact comprising uncertain variables
Beam, H (x) are the coefficient matrixes expressed in the form of the function of x；||Giy||2≤gi TY indicates second order cone constraint；Fx >=f is indicated
It only include the constraint of first stage variable；One equality constraint can be rewritten into two inequality constraints, so above-mentioned compact shape
There is no the forms of equality constraint for formula；λ, π, σ, μ are the dual variable of corresponding constraint in second stage problem.
It is as follows using method for solving of the C&CG algorithm to two stages robust assessment models in step 5:
(1) (2) are decomposed, following primal problem and subproblem form can be obtained:
1) primal problem
The primal problem of two stages robust Model has following form:
In formula: η is the auxiliary variable introduced；L indicates the number of iterations；ylAnd ulIt indicates to introduce when the l times iteration to primal problem
New variables and most bad scene；O is a set, stores the feasible number cut；In primal problem, x and ylFor decision variable, no
Determine that the value of parameter is fixed；
2) subproblem
The subproblem of two stages robust Model has following form:
(2) dualistic transformation is carried out to subproblem:
In formula: λ, π, μ, σ are the dual variable that dual problem introduces；
(3) subproblem after dualistic transformation is linearized: includes u in subproblemTThis bilinear terms of π, and dual variable
Related constraint constitutes a polyhedron collection, if there are optimal solutions to obtain at polyhedron pole for subproblem.It is uncertain
Gather as follows:
In formula:For the predicted value of uncertain parameter, i.e. central value；θ+,θ-It is 0-1 variable, when its value is respectively
(0,0), (0,1), when (1,0), uncertain parameter obtains predicted value, lower and upper limit respectively.
U' is updated in formula, is obtained:
Last remains as bilinear terms to formula (B8) objective function, is handled using large M:
In formula: M is a sufficiently large positive real number.B1And B2For the auxiliary variable of introducing；
Then, formula is variable are as follows:
||σi||2≤μi
-Mθ+≤B1≤Mθ+
π-M(1-θ+)≤B1≤π+M(1-θ+)
-Mθ-≤B2≤Mθ-
π-M(1-θ-)≤B2≤π+M(1-θ-)
θ+,θ-∈{0,1}
θ++θ-≤1
∑(θ++θ-)=Γ
λ, μ >=0, π, σ are free variable (10)
Formula is a MIXED INTEGER Second-order cone programming model；
(4) the upper limit value UB=+ ∞ of robust Model is set, and lower limit value LB=- ∞, the number of iterations l are set as 1, create set O
={ 1 }.
(5) initial " most severe " the scene u of setting1, primal problem formula is solved, optimal solution x is obtained*With optimal value η*；More new lower bound
LB=max { LB, bTx*+η*}；
(6) x that will be acquired*It substitutes into subproblem and solves formula, obtain " most severe " scene u*With optimal value f (x*)；More new model
Upper limit value UB=min { UB, bTx*+f(x*)}；
(7) if UB-LB < ε, then it is assumed that have found robust solution, exit iteration；Otherwise:
1) if subproblem has solution: enabling ul+1=u*, and introduce new variables yl+1Be tied in primal problem as follows, continue
Iteration, and l=l+1 is updated, O=O ∪ { l+1 }.
2) if subproblem is without solution: enabling ul+1=u*, and introduce new variables yl+1Be tied in primal problem as follows, continue
Iteration, and update l=l+1；
Beneficial effect
Distribution network and heating network in facing area integrated energy system of the present invention, effectively meter and integrated energy system
The uncertainty of network constraint and solar energy resources, the available solar energy maximum digestion capability ensured under security of system,
It is improving and supplementing to existing solar energy digestion capability appraisal procedure.Be conducive to instruct the effective use of solar energy resources.
Detailed description of the invention
Fig. 1 is flow chart of the present invention.
Fig. 2 is example system construction drawing in the present invention.
Fig. 3 is algorithm iteration process schematic of the present invention.
Specific embodiment
With reference to the accompanying drawing and specific embodiment the present invention is described further, but following embodiments are absolutely not to this hair
It is bright to have any restrictions.
A kind of regional complex energy resource system solar energy digestion capability appraisal procedure is present embodiments provided, this method is established
In one regional complex energy resource system structure, system includes the distribution network and 32 nodes of 9 nodes.It is shared in system comprehensive at three
Energy source station, stand in be mounted on back pressure type CHP unit and gas fired-boiler (HB), the back pressure type CHP unit thermoelectricity in each energy source station
Than 1.3, maximum output electrical power 0.5MW, gas fired-boiler maximum heat power output 0.5MW.Assume that energy source station can install photovoltaic simultaneously
Generate electricity (PV) and solar thermal collector (SC) equipment.Incident photon-to-electron conversion efficiency is 17.5%, photothermal conversion efficiency 50%.
Step 1: it establishes regional complex energy resource system powering device model: establishing CHP unit model, gas fired-boiler respectively
Model, photovoltaic and heat collector power output uncertainty models, energy storage device model.
(1) the thermo-electrically power output relational expression of back pressure type CHP unit are as follows:
In formula: Hi,chpIt is the heating power output power of back pressure type CHP unit i, unit MW；Pi,chpIt is back pressure type CHP unit i
Electrical output power, unit MW；ci,mFor the hotspot stress of back pressure type CHP unit i.
(2) the units limits equation of gas fired-boiler are as follows:
In formula:Indicate that the heating power of gas fired-boiler i exports the upper limit.
(3) solar energy resources are widely distributed, are one of renewable energy most commonly used at present.The utilization of solar energy
Mode mainly has photovoltaic power generation, solar-heating two ways.The deterministic models of solar energy equipment are as follows:
1) photovoltaic power generation equipment model
In formula: Ai,pvIndicate the mounting area of the photovoltaic system of node i；IiIndicate light when running at node i
According to intensity；ηpvIndicate the incident photon-to-electron conversion efficiency of photovoltaic generating system；Ci,pvIndicate the photovoltaic system installed capacity of node i, value
It is equal with the practical power output of the photovoltaic system under standard test environment.
2) solar thermal collector device model
In formula: Hi,scIndicate the heat power output of node i solar energy heat distribution system；ηscIndicate the efficiency of solar thermal collector；
Ai,scIndicate the mounting area of node i solar thermal collector；Ci,scIndicate the solar thermal collector capacity installed at node i, value
Equal to the solar heat power output under standard test environment.
(4) the uncertain set of building solar energy resources, and the deterministic models in (3) are rewritten as uncertainty models.
In formula:Indicate practical intensity of illumination；It indicates prediction intensity of illumination, can be obtained by meteorological data；Δ I is according to reality
The range of disturbance of the intensity of illumination of border situation setting belongs to uncertain collection U.
Photovoltaic and the ambiguous model of heat collector power output may be expressed as:
Uncertainty parameter Γ is introduced to adjust the conservative and robustness of model.The uncertain set of boxlike so just changes
For the uncertain collection of polyhedron.Each uncertain variables should also meet following uncertainty constraint:
In formula: k is the set in space or time that uncertain variable is related to, the node set accessed such as solar energy equipment
Or illumination duration sets.Expression can at most have Γ uncertain parameter to reach boundary.When Γ value is 0, model is moved back
Turn to deterministic models.
(5) energy storage device model, including electric energy storage device and hot energy storage device are constructed.
1) electric energy storage device model is established:
Si,t+1=Si,t+ΔtPes,i,t
In formula: Si,tIndicate the electric energy storage device at node i in the capacity of t moment；Pes,i,tIndicate the electric energy storage device at node i
In the charge-discharge electric power of t moment, work as Pes,i,tTo indicate charging when positive number, electric discharge is indicated when being negative；Indicate electric energy storage device
Maximum allowable charge power, be positive number；It indicates the maximum allowable discharge power of electric energy storage device, is negative.
2) hot energy storage device model is established:
TSi,t+1=TSi,t+ΔtHts,i,t
In formula: TSi,tIndicate the heat accumulation equipment at node i in the capacity of t moment；Hts,i,tIndicate that the heat accumulation at node i is set
The standby charge and discharge thermal power in t moment, works as Hts,i,tTo indicate to fill heat when positive number, heat release is indicated when being negative；Indicate that heat accumulation is set
It is standby it is maximum allowable fill thermal power, be positive number；It indicates the maximum allowable heat release power of heat accumulation equipment, is negative.
Step 2: regional complex energy resource system energy supply network model is established: using Distflow second order Based On The Conic Model to distribution
Net is modeled, and is modeled using linear heat supply network energy flow model to therrmodynamic system.
(1) Distflow second order cone distribution network model is established:
Pi=Pi,chp+Pi,pv+Pgrid-Pi,load-Pi,es
Qi=Qi,chp+Qgrid-Qi,load
The constraint of node voltage and branch current are as follows:
In formula: δ (j) is using j as the branch headend node set of endpoint node；ξ (j) is using j as the branch of headend node
Set of end nodes；PijAnd QijThe respectively node i active and reactive power that flows to node j；PiAnd QiIt is having for node i respectively
Function and idle injecting power；rijAnd xijThe respectively resistance of route ij and reactance；IijFor the current amplitude on route ij；UiFor
The voltage magnitude of node i；ui,iijFor the auxiliary variable of introducing,PgridAnd QgridRespectively bulk power grid is infused
The active and reactive power entered, the only equation at balance nodes have this；Qi,chpIndicate that CHP unit is idle out at node i
Power；Pi,loadAnd Qi,loadActive and load or burden without work respectively at node i；U andRespectively node allow upper voltage limit and
Lower limit；The maximum current allowed for route.
(2) heat supply network energy flow model is established:
In formula: j ∈ I indicates the node j, H being connected directly with node is,iFor the thermal power for injecting node i；H'ijTo supply water
Pipeline i-j flow into node i heating agent included using thermal power；ΔHj'iFor the loss of thermal power in pipeline ji, work as TsNo
Into constant in the case where change；Respectively minimum, the maximum thermal power that can transmit of pipeline section；∑ R is heating agent to surrounding
The thermal resistance of the every km pipeline of medium；For the permitted maximum flow rate of pipeline section；SijFor pipeline section cross-sectional area；Ts, Tr, TaRespectively
For supply water temperature, return water temperature and environment temperature；lijFor duct length；cpFor fluid specific heat capacity；ρ is fluid density.
Step 3: input energy sources network parameter, optional equipment parameter, electrothermal load curve, the fluctuation range of intensity of illumination.
1) energy network parameter: system includes the distribution network and 32 nodes of 9 nodes.Comprehensive energy at three is shared in system
It stands.Power distribution network node voltage is constrained to 0.9-1.1p.u, and branch current is constrained to 200A.The maximum working medium stream that heat-net-pipeline allows
Speed is 2m/s.System construction drawing is shown in Fig. 1
1 parameters of electric power system of table
2 therrmodynamic system parameter of table
2) optional equipment parameter: the equipment that each energy source station can be installed have CHP unit, photovoltaic power generation, solar energy heating,
Gas fired-boiler, energy storage device.Wherein, the maximum electricity power output of CHP unit is 0.5MW, hotspot stress 1.3.To illustrate the side of being mentioned herein
The effect of method, is arranged 5 scenes herein, and different equipment can be selected in different scenes.
Scene 1: system only configures CHP unit, PV and SC equipment, and electric power deficiency is supplied by higher level's power grid；
Scene 2: on the basis of scene 1, three energy source stations respectively increase the gas fired-boiler with a maximum power 0.2MW
(HB)；
Scene 3: on the basis of scene 1, three energy source stations respectively increase with a maximum charge-discharge electric power 0.1MW, capacity
The electric energy storage device of 0.3MWh；
Scene 4: on the basis of scene 1, three energy source stations respectively increase with a maximum storage thermal power 0.1MW, capacity
The heat accumulation equipment of 0.3MWh；
Scene 5: on the basis of scene 1, three energy source stations respectively increase with a maximum charge-discharge electric power 0.1MW, capacity
The electric energy storage device of 0.3MWh, and maximum storage thermal power 0.1MW, the heat accumulation equipment of capacity 0.3MWh；
3) data of load are inputted:
4 load data of table
4) set the prediction Value Data and intensity of illumination fluctuation range of intensity of illumination: the fluctuation range of intensity of illumination can basis
Historical data is set.10% is set as in this paper example.The prediction Value Data of intensity of illumination such as table 4:
4 intensity of illumination of table predicts Value Data
Step 4: regional complex energy resource system solar energy digestion capability two stages robust assessment models: outer layer model are established
Objective function be solar energy equipment installed capacity it is maximum.In view of the energy figure of electricity, heat is different, using thermodynamics second
Law gives solar energy heat collection equipment one value coefficient, and the value coefficient is related with supply water temperature and environment temperature, for water temperature
Degree is set as 90 DEG C, and environment temperature is set as 0 DEG C.Internal layer is a minimax problem, and objective function is that grid net loss is minimum.
Step 5: it is solved using two stages robust assessment models model of the C&CG algorithm to building.
5 kinds of scene results by calculating are as follows:
The solar energy digestion capability of 5 different scenes of table
1) in scene 1, the heat source of region heat supply network only has CHP unit and SC.The mountable area of maximum of SC and maximum consumption
Power is determined by heating power load (including electric power and heating power network loss).When SC installed capacity is 1.556MW, SC is at 13 moment
Heating power output is intersected with load curve just.In the case that the heating power output of SC determines, limited by heating power balancing the load, CHP
The heat power output of unit is exactly equal to the difference that heating power load and SC heating power export.At the same time, due to back pressure type CHP unit thermo-electrically
The electric power output of coupled characteristic, CHP unit is also determining.In view of system does not allow superior grid transmission, when PV is installed
When capacity reaches 1.949MW, the sum of electrical output power of PV and CHP is exactly equal to the power load of system at this time at 14 moment
Lotus.
2) in scene 2, the gas fired-boiler that capacity is 0.2MW has been installed additional, in 13-14, it is defeated that HB provides part thermal power
Out, so that CHP unit keeps 0 power output state in the period, to provide additional space for photoelectricity consumption.The installation of PV is held
Amount increases 1.991MW by the 1.949MW of scene 1.Photo-thermal is dissolved, since the mounting area of SC is mainly by heating power load
Limitation, therefore photo-thermal digestion capability does not change relatively with scene 1.
3) in scene 3, the electric energy storage device of installation can charge in 10-16, equal to improving electric load.To
Allow the maximum mounting area of system PV 2.287MW is increased to by the 1.949MW of scene 1.But the increase of electric energy storage device
There is no any influence for the thermodynamic equilibrium of system, therefore photo-thermal digestion capability is identical as scene 1.
4) in scene 4, the heat accumulation equipment of installation can carry out heat accumulation in 11 to 16 periods, equal to improving thermic load.From
And the maximum mounting area of system SC is allowed to increase to 1.992MW by the 1.556MW of scene 1.Simultaneously as heat-storing device
Addition, CHP unit CHP unit within 11 to 16 periods keep 0 power output state so that the maximum mounting area of PV is by scene
1 1.949MW increases 1.991MW.
5) in scene 5, system increases heat accumulation and electric energy storage device, PV the and SC mounting area and corresponding consumption of system simultaneously
Ability has obtained corresponding growth.Photoelectricity digestion capability increases to 2.439MW by the 1.949MW of scene 1.Photo-thermal digestion capability
1.992MW can be increased to by the 1.556MW of scene 1.
In conclusion not influencing since HB can only export thermal energy on the consumption of photo-thermal, but reduction can be passed through
The power output of CHP unit provides more spaces for photoelectricity consumption.Electric energy storage device and heat accumulation equipment are equivalent to by energy storage behavior
Electricity, the thermic load of system in a disguised form are improved, therefore has significantly to be promoted for electric, the hot digestion capability for increasing system and make
With especially heat accumulation equipment also has obvious castering action to photoelectricity consumption by the coupling of CHP unit.Simultaneously
Using electric energy storage and heat accumulation equipment, solar energy digestion capability can be preferably promoted.
On the basis of scene 5, the value of different uncertainties is set, analyzes its influence to final result.Consider
There are illumination in intensity of illumination curve a total of 13 hours chosen to this example, therefore maximum uncertainty is 13.It is uncertain
The number at the time of numerical value for spending budget indicates to reach intensity of illumination fluctuation boundary.
Through analytical calculation, the calculated result under different uncertainties is as shown in table 6.
Solar energy digestion capability under the different uncertainties of table 6
With the promotion of uncertainty, the installed capacity of PV and SC are not to be gradually reduced, but be in uncertainty
5, it is mutated at 6 liang.
Found through analysis, restrict PV and SC maximum installed capacity constraint it is main there are two, first is that the power output of PV and SC is not
It can exceed that the maximum storage power of load and energy storage；Second is that the solar energy of consumption is beyond negative within the intensity of illumination biggish period
The maximum that the energy of part needed for lotus is less than energy storage device can memory capacity.Meanwhile under the premise of considering that illumination is uncertain,
Restrict PV and SC maximum installed capacity is a small number of periods close to noon.With the increase of uncertainty, PV and SC is limited
The operative constraint condition of maximum installed capacity is changed.Fig. 2 is illustrated as uncertainty increases, and C&CG algorithm search is most
The process of bad illumination scene.When uncertainty is 1, intensity of illumination upper bound when algorithm is by 13 and 14 has been added to robust planning about
Among beam, at the time of this illustrates that the two moment are the influence maximums to solar energy consumption.When uncertainty rises to 4,12-
It is to influence the maximum period when 15.So far, the major constraints for restricting solar energy consumption are the maximum charge and discharges of load and energy storage
Power.When uncertainty is equal to 5 and 6, the period for influencing solar energy consumption at this time is 11-15 and 11-16, at this time major constraints
The maximum for being transformed to energy storage in the period can memory capacity.When uncertainty is greater than 6, solar energy consumption amount is no longer changed,
The intensity of illumination fluctuation for illustrating remaining period is not effectively to constrain for solar energy consumption.In addition, it can be seen from the figure that C&
CG algorithm can be found that the operative constraint condition so that target function value deterioration, and has faster convergence property, usually 2
It can be restrained after~4 iteration.The period is the active constraint period in dotted line in Fig. 2.
Claims (3)
1. a kind of regional complex energy resource system solar energy digestion capability appraisal procedure, which is characterized in that including what is carried out in order
The following steps:
Step 1: establish regional complex energy resource system powering device model: establish respectively CHP unit model, gas fired-boiler model,
Photovoltaic and heat collector power output uncertainty models, energy storage device model；
Step 2: establish regional complex energy resource system energy supply network model: using Distflow second order Based On The Conic Model to power distribution network into
Row modeling, models therrmodynamic system using linear heat supply network energy flow model；
Step 3: corresponding model obtains energy network parameter, optional equipment parameter, electrothermal load from step 1 and step 2
Parameter of curve, the fluctuation range of the prediction Value Data and intensity of illumination of intensity of illumination；
Step 4: regional complex energy resource system solar energy digestion capability two stages robust assessment models: the mesh of outer layer model are established
Scalar functions are that the installed capacity of solar energy equipment is maximum；The objective function of interior layer model is that grid net loss is minimum；
Step 5: it is solved using robust assessment models of the C&CG algorithm to building.
2. a kind of regional complex energy resource system solar energy digestion capability appraisal procedure according to claim 1, feature exist
In: in step 4, solar energy digestion capability two stages robust assessment models objective function is as follows:
In formula, τ indicates the value coefficient of thermal energy,TaIndicate environment temperature, TsIndicate supply water temperature；ω is power grid
The weight coefficient of network loss.PlossThe total power grid damage of expression system；I indicates the place of installation solar energy equipment；X, y are the first rank respectively
The decision variable of section and second stage；After Ω (x, u) is one group (x, u) given, the feasible zone of y；
To objective function compact form:
Constraint condition has compact form below:
In formula, Dy >=d indicates constraint only comprising second stage variable；H (x) y=u indicates the constraint comprising uncertain variables, H
It (x) is the coefficient matrix expressed in the form of the function of x；Indicate second order cone constraint；Fx >=f is indicated
The constraint of first stage variable；One equality constraint can be rewritten into two inequality constraints, so above-mentioned compact form is not
There is the form of equality constraint；λ, π, σ, μ are the dual variable of corresponding constraint in second stage problem.
3. a kind of regional complex energy resource system solar energy digestion capability appraisal procedure according to claim 1, feature exist
In: as follows using method for solving of the C&CG algorithm to two stages robust assessment models in step 5:
(1) (2) formula is decomposed, following primal problem and subproblem form can be obtained:
1) primal problem
The primal problem of two stages robust Model has following form:
In formula: η is the auxiliary variable introduced；L indicates the number of iterations；ylAnd ulIt introduces when indicating the l times iteration to primal problem new
Variable and most bad scene；O is a set, stores the feasible number cut；In primal problem, x and ylFor decision variable, do not know
The value of parameter is fixed；
2) subproblem
The subproblem of two stages robust Model has following form:
(2) dualistic transformation is carried out to subproblem:
In formula: λ, π, μ, σ are the dual variable that dual problem introduces；
(3) subproblem after dualistic transformation is linearized: includes u in subproblemTThis bilinear terms of π, and dual variable is related
Constraint constitutes a polyhedron collection, if there are optimal solutions to obtain at polyhedron pole for subproblem, does not know set
It is as follows:
In formula:For the predicted value of uncertain parameter, i.e. central value；θ+,θ-Be 0-1 variable, when its value be respectively (0,0),
(0,1), when (1,0), uncertain parameter obtains predicted value, lower and upper limit respectively.U' is updated in formula, is obtained:
Last remains as bilinear terms to formula (B8) objective function, is handled using large M:
In formula: M is a sufficiently large positive real number.B1And B2For the auxiliary variable of introducing；
Then, formula is variable are as follows:
||σi||2≤μi
-Mθ+≤B1≤Mθ+
π-M(1-θ+)≤B1≤π+M(1-θ+)
-Mθ-≤B2≤Mθ-
π-M(1-θ-)≤B2≤π+M(1-θ-)
θ+,θ-∈{0,1}
θ++θ-≤1
∑(θ++θ-)=Γ
λ, μ >=0, π, σ are free variable (10)
Formula is a MIXED INTEGER Second-order cone programming model；
(4) the upper limit value UB=+ ∞ of robust Model is set, and lower limit value LB=- ∞, the number of iterations l are set as 1, create set O=
{1}。
(5) initial " most severe " the scene u of setting1, primal problem formula is solved, optimal solution x is obtained*With optimal value η*；More new lower bound LB=
max{LB,bTx*+η*}；
(6) x that will be acquired*It substitutes into subproblem and solves formula, obtain " most severe " scene u*With optimal value f (x*)；More new model it is upper
Limit value UB=min { UB, bTx*+f(x*)}；
(7) if UB-LB < ε, then it is assumed that have found robust solution, exit iteration；Otherwise:
1) if subproblem has solution: enabling ul+1=u*, and introduce new variables yl+1Be tied in primal problem as follows, continue iteration,
And l=l+1 is updated, O=O ∪ { l+1 }；
2) if subproblem is without solution: enabling ul+1=u*, and introduce new variables yl+1Be tied in primal problem as follows, continue iteration,
And update l=l+1；
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| CN114759602A (en) * | 2022-04-24 | 2022-07-15 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance capacity assessment method considering photovoltaic extreme scene |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111463838A (en) * | 2020-05-07 | 2020-07-28 | 国网山东省电力公司经济技术研究院 | Two-stage robust optimization scheduling method and system considering energy storage participation in secondary frequency modulation |
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| CN112886572A (en) * | 2021-01-21 | 2021-06-01 | 三峡大学 | Evaluation method for renewable energy consumption capability of power grid |
| CN114759602A (en) * | 2022-04-24 | 2022-07-15 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance capacity assessment method considering photovoltaic extreme scene |
| CN114759602B (en) * | 2022-04-24 | 2024-04-05 | 国网山东省电力公司潍坊供电公司 | Power distribution network acceptance assessment method considering photovoltaic extreme scenes |
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Application publication date: 20191112 |