CN109713715A - A kind of hot stored electric heating load control method based on mixed integer dynamic optimization - Google Patents
A kind of hot stored electric heating load control method based on mixed integer dynamic optimization Download PDFInfo
- Publication number
- CN109713715A CN109713715A CN201811509137.4A CN201811509137A CN109713715A CN 109713715 A CN109713715 A CN 109713715A CN 201811509137 A CN201811509137 A CN 201811509137A CN 109713715 A CN109713715 A CN 109713715A
- Authority
- CN
- China
- Prior art keywords
- electric heating
- heating load
- stored electric
- wind
- hot stored
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005485 electric heating Methods 0.000 title claims abstract description 96
- 238000005457 optimization Methods 0.000 title claims abstract description 52
- 238000000034 method Methods 0.000 title claims abstract description 37
- 230000005611 electricity Effects 0.000 claims abstract description 60
- 238000013178 mathematical model Methods 0.000 claims abstract description 18
- 230000029087 digestion Effects 0.000 claims abstract description 14
- 230000007246 mechanism Effects 0.000 claims abstract description 4
- 238000004146 energy storage Methods 0.000 claims description 19
- 238000007493 shaping process Methods 0.000 claims description 11
- 230000008878 coupling Effects 0.000 claims description 10
- 238000010168 coupling process Methods 0.000 claims description 10
- 238000005859 coupling reaction Methods 0.000 claims description 10
- 238000005338 heat storage Methods 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 7
- 238000009825 accumulation Methods 0.000 claims description 4
- 230000001276 controlling effect Effects 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 4
- 230000001105 regulatory effect Effects 0.000 claims description 4
- 230000009194 climbing Effects 0.000 claims description 3
- 238000012512 characterization method Methods 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000005684 electric field Effects 0.000 claims description 2
- 239000002356 single layer Substances 0.000 claims description 2
- 230000007812 deficiency Effects 0.000 abstract description 3
- 238000005265 energy consumption Methods 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 7
- 230000006872 improvement Effects 0.000 description 6
- 230000008859 change Effects 0.000 description 4
- 235000013399 edible fruits Nutrition 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 239000003245 coal Substances 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000010438 heat treatment Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The hot stored electric heating load control method based on mixed integer dynamic optimization that the invention discloses a kind of belongs to power grid new energy consumption field.The following steps are included: S1, determining that the consumption of hot stored electric heating load is obstructed the mechanism of wind-powered electricity generation, and influence of the hot stored electric heating load control characteristic to wind electricity digestion is analyzed；S2, the dual-mode FM section mathematical model for establishing hot stored electric heating load；S3, on the basis of step S1 and S2, determine the hot stored electric heating load control mathematical model based on wind electricity digestion；S4, it is designed by improving PSO algorithm, the mixed integer dynamic optimization problem of foundation is solved.Finally obtain the control method of hot stored electric heating load consumption wind-powered electricity generation.The progress control method is while excavating its regulation potentiality, consider the diversity of hot stored electric heating load control model, the comfort level of user is ensured, compensate for the deficiency that current hot stored electric heating load is adjusted, the consumption amount for improving grid connected wind power simultaneously, compensates for the deficiency of current hot stored electric heating load control method.
Description
Technical field
The present invention relates to power system load control technology fields, and in particular, to one kind is excellent based on MIXED INTEGER dynamic
The hot stored electric heating load of change dissolves wind-powered electricity generation control method.
Background technique
The last one link of load as electric system, plays vital work during electric energy Real-time Balancing
With.Under traditional distribution mode, dispatching of power netwoks department foundation predicts load, is controlled by the adjusting to conventional power unit, real
The Real-time Balancing of existing grid power.In recent years, as China's new energy develops rapidly, wind-powered electricity generation permeability rises year by year, due to wind
The intermittence that can have, randomness, demodulates the features such as peak at fluctuation, so that Generation Side accurate controllably is become to fluctuate by original
And it is difficult to predict, resource adjustments energy power limit is adjusted by traditional power grid, considers power network safety operation, current big in China
Scale wind power base often faces abandonment problem, causes the great wasting of resources.On the other hand, by clean energy resource application and popularizations,
There are a large amount of preferable hot stored electric heating loads of regulation performance in wind-powered electricity generation area of concentration
Limited in wind electricity digestion capability, extensive energy storage technology is not yet broken through, and the installed capacity of wind-driven power of areal is continuous
Expand, accumulation of heat electric heating is gradually under the status of substitutionization coal fired boiler.Hot stored electric heating load is introduced to the optimization of wind electricity digestion
Scheduling problem realizes wind-powered electricity generation for thermal control, it can be achieved that the mutual supplement with each other's advantages of large-scale wind power and Demand-side wide area load, coordination hair
Exhibition.
It is, therefore, desirable to provide a kind of control method is come when solving using wind-powered electricity generation heat supply, the control of hot stored electric heating load is asked
Topic meets the heating effect of accumulation of heat electric heating, while promoting the grid connected wind power on-site elimination that is obstructed, and that improves wind-powered electricity generation heat supply utilizes effect
Rate.
Summary of the invention
In view of the deficiencies of the prior art, the present invention provides a kind of hot stored electric heating load based on mixed integer dynamic optimization
Wind-powered electricity generation control method is dissolved, using the adjustable potentiality of hot stored electric heating load, power grid is improved to the digestion capability of wind-powered electricity generation, alleviates
Adjusting pressure of the conventional energy resource to wind-powered electricity generation.
Technical scheme is as follows:
A kind of hot stored electric heating load consumption wind-powered electricity generation control method based on mixed integer dynamic optimization, including walk as follows
It is rapid:
S1, it determines that hot stored electric heating load dissolves the mechanism for the wind-powered electricity generation that is obstructed, analyzes hot stored electric heating load control characteristic pair
The influence of wind electricity digestion；
S2, the dual-mode FM section mathematical model for establishing hot stored electric heating load；
S3, on the basis of step S1 and S2, determine the hot stored electric heating load control mathematical model based on wind electricity digestion；
S4, it is designed by improving PSO algorithm, the mixed integer dynamic optimization problem of foundation is solved, is finally obtained
The control method of hot stored electric heating load consumption wind-powered electricity generation.
Further, the adjusting mathematical model of the hot stored electric heating load of foundation described in step S2 specifically:
S201. the active model of hot stored electric heating load established
S202. hot stored electric heating load i is established in the energy storage state model of t moment
S203. hot stored electric heating load mathematical model in participating in adjustment process needs to meet constraint
(a) adjustable range constrains: limiting within the scope of pondage, can be carried out continuously adjusting.
(b) energy storage state constrains: considering that the energy storage characteristic of heat-storage medium, thermal storage electric boiler usually have the most senior engineer of setting
Make temperature, energy storage state constraint need to be met
(c) fluctuate rate constraint: the fluctuation for the safe and stable operation for ensuring electric boiler, power should be limited in certain model
Within enclosing.
Further, the hot stored electric heating load control mathematical model described in step S3 based on wind electricity digestion is specific
Are as follows:
S301. objective function is that consumption wind-powered electricity generation amount is maximum
S302. it needs to meet constraint condition and includes:
(a) hot stored electric heating load regulating power constrains:
(b) wind power plant operation, which constrains, includes:
1. wind power output bound constrains:
2. considering Blade inertia and wind-powered electricity generation fluctuating factor in view of Wind turbines are to contain rotary part, transported in plan
Wind-powered electricity generation climbing rate will meet bound constraint when row:
(c) system active balance constrains:
(d) hot stored electric heating load reconciles mode 0-1 variable bound:
For hot stored electric heating load, the present invention is adjusted according to actual conditions using double models that adjust, and is introduced discrete
Variable Kadj,i(t) as shaping modes coefficient, the constraint of the discrete variable value need to be considered when adjusting.
By solving model, the power output of plan a few days ago of hot stored electric heating load can be obtained.
Further, improvement PSO algorithm described in step S4 designs specifically:
S401. assume that the model A for needing to solve is mixed integer dynamic optimization problem, for equality constraint, introduce small just
Number ε converts inequality constraints for equality constraint as tolerance.It is the planning being converted into containing only p+q inequality constraints by A
Problem.
S402. first 0-1 integer variable in model relax as continuous variable in [0,1] section, convert problem to
Continuous variable optimization problem obtains state and is solved；
S403. bi-distribution is introduced to judge that the 0-1 variable after relaxation is to be under the jurisdiction of shaping modes 1, or reconcile mode
0.Compared with tradition is rounded rounding method, should be rounded method by bi-distribution stochastic model has stronger robustness and adaptive
Ying Xing enhances the variation ability of integer variable.To further increase population diversity, global optimizing ability is improved, to Kadji(t)
Solution vector, is defined as by multiple random floor operation respectively
S404. adjusted result is further corrected and solves time coupling constraint, obtain optimal 0-1 variable optimization knot
Fruit.K′adj,i(t) it is decision variable value after amendment, is the conciliation mode decision change that can satisfy time coupling constraint after solving
Amount combination optimal solution.
S405 is carried it into objective function and constraint matrix after completing the optimization of 0-1 variable, and the former variable containing 0-1 mixes
It closes integer optimization problems and is converted into continuous variable optimization problem, and then can be solved using PSO.After solution, obtain
The plan power output P of hot stored electric heating load after to adjustingc_k(t), t=1,2 ..., 96.
The present invention has the advantages that
(1) it in mixed integer optimization problem solving, is designed, is solved in conventional combination optimization problem by innovatory algorithm
Integer variable and Continuous Variable Problems cannot be handled simultaneously, and algorithm design is simple and effective.
(2) when modeling to hot stored electric heating load regulating power, consider two kinds of conciliation modes.Both consider to continuously adjust,
It also allows for and is adjusted by group switching, more identical with actual conditions, also more conducively accumulation of heat electric heating participates in adjusting.It is controlled establishing
When mathematical model processed, wind electricity digestion demand is not only considered, while considering system restriction, therefore, calculated result has implementable
Property, carrying out load control system to power grid has preferable directive significance.
Detailed description of the invention
Below by drawings and examples, technical scheme of the present invention will be described in further detail.
Fig. 1 is that the present invention is based on the processes of the hot stored electric heating load of mixed integer dynamic optimization consumption wind-powered electricity generation control method
Figure；
Fig. 2 is that the present invention is based on the frames of the hot stored electric heating load of mixed integer dynamic optimization consumption wind-powered electricity generation control method
Figure；
Fig. 3 is that hot stored electric heating load adjusts the active schematic diagram of model in specific embodiment；
Fig. 4 is that hot stored electric heating load adjusts model electricity schematic diagram in specific embodiment；
Fig. 5 is the improvement PSO algorithm design flow diagram of mixed integer dynamic optimization problem solving in specific embodiment；
Specific embodiment
In order to clearly illustrate technical solution of the present invention, by the detailed process of the invention of detailed description below.Obviously,
The specific implementation of present example is not limited to specific details appreciated by those skilled in the art.With reference to the accompanying drawing and
The present invention is further described for example, and embodiments of the present invention are not limited thereto.
As shown in Figure 1, a kind of hot stored electric heating load based on mixed integer dynamic optimization of the present embodiment dissolves wind-powered electricity generation control
Method processed, includes the following steps:
S1, it determines that hot stored electric heating load dissolves the mechanism for the wind-powered electricity generation that is obstructed, analyzes hot stored electric heating load control characteristic pair
The influence of wind electricity digestion；
S2, the dual-mode FM section mathematical model for establishing hot stored electric heating load；
S3, on the basis of step S1 and S2, determine the hot stored electric heating load control mathematical model based on wind electricity digestion；
S4, it is designed by improving PSO algorithm, the mixed integer dynamic optimization problem of foundation is solved, is finally obtained
The control method of hot stored electric heating load consumption wind-powered electricity generation.
Further, the adjusting mathematical model of the hot stored electric heating load of foundation described in step S2 specifically:
S201. the active model of hot stored electric heating load established
S202. hot stored electric heating load i is established in the energy storage state model of t moment
S203. hot stored electric heating load mathematical model in participating in adjustment process needs to meet constraint
(a) adjustable range constrains: limiting within the scope of pondage, can be carried out continuously adjusting.
(b) energy storage state constrains: considering that the energy storage characteristic of heat-storage medium, thermal storage electric boiler usually have the most senior engineer of setting
Make temperature, energy storage state constraint need to be met
(c) fluctuate rate constraint: the fluctuation for the safe and stable operation for ensuring electric boiler, power should be limited in certain model
Within enclosing.
Further, the hot stored electric heating load control mathematical model described in step S3 based on wind electricity digestion is specific
Are as follows:
S301. objective function are as follows:
In formula, EWFor the wind-powered electricity generation electricity that is obstructed；PWfcst,i(t) wind-powered electricity generation prediction power output is indicated；Number of segment when T is in dispatching cycle, because
This value T=96, Δ T=15min；PGIt (t) is conventional power unit power output planned value, P in operation planl_base(t) in system not
Adjustable load is active；Pc_k(t) indicate can hot stored electric heating load power output planned value.
S302. it needs to meet constraint condition and includes:
(a) hot stored electric heating load regulating power constrains:
(b) wind power plant operation constraint:
When traffic department formulates output of wind electric field plan, wind power plant i power output PW,i(t) it needs to meet constraint and includes:
1. wind power output bound constrains:
0≤PW,i(t)≤PWfcst,i(t) (3)
P in formulaWfcst,iIt (t) is power prediction value, i.e. the capacity declared to scheduling of wind power plant, traffic department is to wind-powered electricity generation
Field plan power output is less than the value.
2. considering Blade inertia and wind-powered electricity generation fluctuating factor in view of Wind turbines are to contain rotary part, transported in plan
Wind-powered electricity generation climbing rate will meet bound constraint when row:
In formulaWithRespectively indicate wind power plant i or more Ramp Rate limiting value.
(c) system active balance constrains:
(d) hot stored electric heating load reconciles mode 0-1 variable bound:
For hot stored electric heating load, the present invention is adjusted according to actual conditions using double models that adjust, and is introduced discrete
Variable Kadj,i(t) as shaping modes coefficient, the constraint of the discrete variable value need to be considered when adjusting.
In the model of above-mentioned foundation, each variable is the amount changed over time, establishes a multi-period dynamic optimization tune
Spend model.It is a single-object problem in view of the Optimized model target is that wind-powered electricity generation is obstructed electricity minimum, model constraint packet
Equality constraint inequality constraints is included, it is typical hybrid variable dynamic optimization that types of variables, which includes real variable and discrete variable,
Solve problems.By solving model, the power output of the plan a few days ago P of hot stored electric heating load can be obtainedc_k(t), t=1,2 ...,
96。
Further, improvement PSO algorithm described in step S4 designs specifically:
S401. assume that the model A for needing to solve is mixed integer dynamic optimization problem, for equality constraint, introduce small just
Number ε converts inequality constraints for equality constraint as tolerance.It is the planning being converted into containing only p+q inequality constraints by A
Problem.
S402. first 0-1 integer variable in model relax as continuous variable in [0,1] section, convert problem to
Continuous variable optimization problem obtains state and is solved；
S403. bi-distribution is introduced to judge that the 0-1 variable after relaxation is to be under the jurisdiction of the mode of continuously adjusting, or grouping is thrown
Cut conciliation mode.Compared with tradition is rounded rounding method, should be rounded method by bi-distribution stochastic model has stronger Shandong
Stick and adaptivity enhance the variation ability of integer variable.To further increase population diversity, global optimizing energy is improved
Power, to Kadj,i(t) solution vector, is defined as by multiple random floor operation respectively
S404. adjusted result is further corrected and solves time coupling constraint, obtain optimal 0-1 variable optimization knot
Fruit.K′adj,i(t) it is decision variable value after amendment, is the conciliation mode decision change that can satisfy time coupling constraint after solving
Amount combination optimal solution.
S405 is carried it into objective function and constraint matrix after completing the optimization of 0-1 variable, and the former variable containing 0-1 mixes
It closes integer optimization problems and is converted into continuous variable optimization problem, and then can be solved using PSO.After solution, obtain
The plan power output P of hot stored electric heating load after to adjustingc_k(t), t=1,2 ..., 96.
Further, Fig. 2 is that wind-powered electricity generation control is dissolved the present invention is based on the hot stored electric heating load of mixed integer dynamic optimization
The frame diagram of method.
Fig. 3 is that hot stored electric heating load adjusts the active schematic diagram of model in specific embodiment；
Active model of the hot stored electric heating load i established in step S201 in t moment are as follows:
PC, i(t)=KAdj, i(t)·ηAdj, i(t)·P0, i+(1-KAdj, i(t))·nt(t)·P0, i KAdj, i(t), nt∈
{ 0,1 } (7)
In formula, P0,iFor specified active, the K of hot stored electric heating load iadj,iIt (t) is shaping modes coefficient, ηadj,i(t) it is
Coefficient of discharge is adjusted when participation continuously adjusts.
Hot stored electric heating load is active in step S203 is constrained to
(a) adjustable range constrains: limiting within the scope of pondage, can be carried out continuously adjusting.
0≤Pc,i(t)≤P0,i (9)
(b) fluctuate rate constraint: the fluctuation for the safe and stable operation for ensuring electric boiler, power should be limited in certain model
Within enclosing.
In formula,WithWhen respectively indicating the increase and decrease adjusting of hot stored electric heating load, the rate of fluctuation is most worth.
Fig. 4 is that hot stored electric heating load adjusts model electricity schematic diagram in specific embodiment；
Energy storage state model of the hot stored electric heating load i established in step S202 in t moment are as follows:
In formula, SOCc,iIt (t) is the energy storage state for introducing state-of-charge concept characterization t moment hot stored electric heating load i, Hc,i
(t) the t moment load quantity of heat storage, H are indicatedc,i(t0) it is initial time amount of stored heat, mc,iFor electric heating conversion efficiency, Pg_iIt (t) is confession
Thermic load, Qcap,iFor heat storage capacity, it is Δ t=15min that Δ t is time interval herein.
In step S203, hot stored electric heating load energy storage state constraint are as follows: consider the energy storage characteristic of heat-storage medium, hot stored electric
Boiler usually has the maximum operating temperature of setting, need to meet energy storage state constraint
SOCmin≤SOCc,i(t)≤SOCmax (12)
In formula, SOCmaxAnd SOCminRespectively minimax stored energy ratio.
Fig. 5 is the improvement PSO algorithm design flow diagram of mixed integer dynamic optimization problem solving in specific embodiment；Step
Improvement PSO algorithm design described in rapid S4 specifically:
S401. assume that the model A for needing to solve is mixed integer dynamic optimization problem, it herein will be equivalent with solving model
Are as follows:
In formula, x=[x1,…,xn]T, y=[y1,…,ym]TRespectively continuous variable and integer variable.About for equation
Beam introduces small positive number ε as tolerance, converts inequality constraints for equality constraint in formula (13).
hl(x, y)-ε≤0, l=1 ..., p (14)
In this way, formula (13) is converted into the planning problem containing only p+q inequality constraints.
S402. first 0-1 integer variable in model relax as continuous variable in [0,1] section, convert problem to
Continuous variable optimization problem obtains state and is solved；
The 0-1 integer variable for including in above-mentioned model is hot stored electric heating load shaping modes decision variable Kadj,i(t).It will
It is first converted into the continuous variable in [0,1] section.Constraint condition K in master mouldadj,i(t) ∈ { 0,1 } is converted into formula (15):
Disregard its more period coupling constraint for needing to meet in adjustment process, is converted into the continuous of single layer with master mould
Variable optimization problem.Optimization value is obtained after solution
S403. bi-distribution is introduced to judge that the 0-1 variable after relaxation is under the jurisdiction of shaping modes 1 or shaping modes
0。
Binomial distribution probability model is introduced to judge Kadj,i(t) rounding.
That is Kadj,i(t) 1 probability is taken to be0 probability is taken to beIt is available after floor operation
Kadj,i(t) value vectorCompared with tradition is rounded rounding method, this passes through
Bi-distribution stochastic model, which is rounded method, has stronger robustness and adaptivity, enhances the variation ability of integer variable.For
Population diversity is further increased, global optimizing ability is improved, to Kadji, (t) multiple random floor operation, respectively by solution vector
It is defined as
S404. adjusted result is further corrected and solves time coupling constraint, obtain optimal 0-1 variable optimization knot
Fruit.K′adj,i(t) it is decision variable value after amendment, is the conciliation mode decision change that can satisfy time coupling constraint after solving
Amount combination optimal solution.
S405 is carried it into objective function and constraint matrix after completing the optimization of 0-1 variable, and the former variable containing 0-1 mixes
It closes integer optimization problems and is converted into continuous variable optimization problem, and then can be solved using PSO.After solution, obtain
The power output controlling value P of hot stored electric heating load after to adjustingc_k(t), t=1,2 ..., 96.
Finally, it should be noted that the foregoing is only a preferred embodiment of the present invention, it is not intended to restrict the invention,
Although the present invention is described in detail referring to the foregoing embodiments, for those skilled in the art, still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features.
It is all within the contents of the present invention and principle, any modification, equivalent replacement, improvement and so on should be included in of the invention
Within protection scope.
Claims (4)
1. a kind of hot stored electric heating load based on mixed integer dynamic optimization dissolves wind-powered electricity generation control method, which is characterized in that packet
Include following step:
S1, it determines that hot stored electric heating load dissolves the mechanism for the wind-powered electricity generation that is obstructed, analyzes hot stored electric heating load control characteristic to wind-powered electricity generation
The influence of consumption；
S2, the dual-mode FM section mathematical model for establishing hot stored electric heating load；
S3, on the basis of step S1 and S2, determine the hot stored electric heating load control mathematical model based on wind electricity digestion；
S4, it is designed by improving PSO algorithm, the mixed integer dynamic optimization problem of foundation is solved, accumulation of heat is finally obtained
The control method of electric heating load consumption wind-powered electricity generation.
2. the hot stored electric heating load according to claim 1 based on mixed integer dynamic optimization dissolves wind-powered electricity generation controlling party
Method, which is characterized in that in the step S2, the adjusting mathematical model of the hot stored electric heating load of foundation specifically:
S201. the active model of hot stored electric heating load established are as follows:
PC, i(t)=KAdj, i(t)·ηAdj, i(t)·P0, i+(1-KAdj, i(t))·nt(t)·P0, i KAdj, i(t), nt∈ { 0,1 } (1)
In formula, P0,iFor specified active, the K of hot stored electric heating load iadj,iIt (t) is shaping modes coefficient, ηadj,iIt (t) is the company of participation
Coefficient of discharge is adjusted when continuous adjusting；
S202. hot stored electric heating load i is established in the energy storage state model of t moment are as follows:
In formula, SOCc,iIt (t) is the energy storage state for introducing state-of-charge concept characterization t moment hot stored electric heating load i, Hc,i(t)
Indicate the t moment load quantity of heat storage, Hc,i(t0) it is initial time amount of stored heat, mc,iFor electric heating conversion efficiency, Pg_iIt (t) is heat supply
Load, Qcap,iFor maximum heat storage capacity, it is Δ t=15min that Δ t is time interval herein；
S203. hot stored electric heating load mathematical model in participating in adjustment process, which needs to meet to constrain, includes:
(a) adjustable range constrains: limiting within the scope of pondage, can be carried out continuously adjusting；
0≤Pc,i(t)≤P0,i (4)
(b) energy storage state constrains: considering that the energy storage characteristic of heat-storage medium, thermal storage electric boiler usually have the highest work temperature of setting
Degree need to meet energy storage state constraint
SOCmin≤SOCc,i(t)≤SOCmax (5)
In formula, SOCmaxAnd SOCminRespectively minimax stored energy ratio；
(c) fluctuate rate constraint: for the safe and stable operation for ensuring electric boiler, the fluctuation of power should be limited in a certain range it
It is interior；
In formula,WithWhen respectively indicating the increase and decrease adjusting of hot stored electric heating load, the rate of fluctuation is most worth.
3. the hot stored electric heating load according to claim 1 based on mixed integer dynamic optimization dissolves wind-powered electricity generation controlling party
Method, which is characterized in that in the step S3, the hot stored electric heating load control mathematical model based on wind electricity digestion specifically:
S301. objective function are as follows:
In formula, EWFor the wind-powered electricity generation electricity that is obstructed；PWfcst,i(t) wind-powered electricity generation prediction power output is indicated；Number of segment when T is in dispatching cycle, therefore take
Value T=96, Δ T=15min；PGIt (t) is conventional power unit power output planned value, P in operation planl_baseIt (t) is non-adjustable in system
It is active to save load；Pc_k(t) indicate can hot stored electric heating load power output planned value；
S302. it needs to meet constraint condition and includes:
(a) hot stored electric heating load regulating power constrains:
(b) wind power plant operation constraint:
When traffic department formulates output of wind electric field plan, wind power plant i power output PW,i(t) it needs to meet constraint and includes:
1. wind power output bound constrains:
0≤PW,i(t)≤PWfcst,i(t) (9)
P in formulaWfcst,iIt (t) is power prediction value, i.e. the capacity declared to scheduling of wind power plant, traffic department is to wind power plant meter
It marks power and is less than the value；
2. Blade inertia and wind-powered electricity generation fluctuating factor are considered in view of Wind turbines are to contain rotary part, when planning operation
Wind-powered electricity generation climbing rate will meet bound constraint:
In formulaWithRespectively indicate wind power plant i or more Ramp Rate limiting value；
(c) system active balance constrains:
(d) hot stored electric heating load reconciles mode 0-1 variable bound:
For hot stored electric heating load, the present invention is adjusted according to actual conditions using double models that adjust, and introduces discrete variable
Kadj,i(t) as shaping modes coefficient, the constraint of the discrete variable value need to be considered when adjusting；
In the model of above-mentioned foundation, each variable is the amount changed over time, establishes a multi-period dynamically optimized scheduling mould
Type；It is a single-object problem in view of the Optimized model target is that wind-powered electricity generation is obstructed electricity minimum, model constraint includes etc.
Formula constrains inequality constraints, and types of variables includes real variable and discrete variable, solves for typical hybrid variable dynamic optimization
Problem；By solving model, the power output of the plan a few days ago P of hot stored electric heating load can be obtainedc_k(t), t=1,2 ..., 96.
4. the hot stored electric heating load according to claim 1 based on mixed integer dynamic optimization dissolves wind-powered electricity generation controlling party
Method, which is characterized in that in the step S4, improve the design of PSO algorithm specifically:
S401. assume that the model A for needing to solve is mixed integer dynamic optimization problem, it herein will be equivalent with solving model are as follows:
In formula, x=[x1,…,xn]T, y=[y1,…,ym]TRespectively continuous variable and integer variable；For equality constraint, draw
Enter small positive number ε as tolerance, converts inequality constraints for equality constraint in formula (13)；
hl(x, y)-ε≤0, l=1 ..., p (14)
In this way, formula (13) is converted into the planning problem containing only p+q inequality constraints；
S402. first 0-1 integer variable in model relax as continuous variable in [0,1] section, convert problem to continuously
Variable optimization problem obtains state and is solved；
The 0-1 integer variable for including in above-mentioned model is hot stored electric heating load shaping modes decision variable Kadj,i(t)；By its elder generation
The continuous variable being converted into [0,1] section；Constraint condition K in master mouldadj,i(t) ∈ { 0,1 } is converted into formula (15):
Disregard its more period coupling constraint for needing to meet in adjustment process, the continuous variable of single layer is converted into master mould
Optimization problem；Optimization value is obtained after solution
S403. bi-distribution is introduced to judge that the 0-1 variable after relaxation is to be under the jurisdiction of shaping modes 1, or reconcile mode 0；
Binomial distribution probability model is introduced to judge Kadj,i(t) rounding；
That is Kadj,i(t) 1 probability is taken to be0 probability is taken to beAvailable K after floor operationadj,i
(t) value vectorCompared with tradition is rounded rounding method, this passes through binomial
It is distributed stochastic model and is rounded method with stronger robustness and adaptivity, enhance the variation ability of integer variable；For into one
Step increases population diversity, improves global optimizing ability, right(t) multiple random floor operation, respectively defines solution vector
For
S404. adjusted result is further corrected and solves time coupling constraint, obtain optimal 0-1 variable optimum results；
K′adj,i(t) it is decision variable value after amendment, is the conciliation mode decision set of variables that can satisfy time coupling constraint after solving
Close optimal solution；
S405 is carried it into objective function and constraint matrix after completing the optimization of 0-1 variable, and the mixing of the former variable containing 0-1 is whole
Number optimization problems are converted into continuous variable optimization problem, and then can be solved using PSO；After solution, adjusted
The plan power output P of hot stored electric heating load after sectionc_k(t), t=1,2 ..., 96.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811509137.4A CN109713715A (en) | 2018-12-11 | 2018-12-11 | A kind of hot stored electric heating load control method based on mixed integer dynamic optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811509137.4A CN109713715A (en) | 2018-12-11 | 2018-12-11 | A kind of hot stored electric heating load control method based on mixed integer dynamic optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109713715A true CN109713715A (en) | 2019-05-03 |
Family
ID=66256365
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811509137.4A Pending CN109713715A (en) | 2018-12-11 | 2018-12-11 | A kind of hot stored electric heating load control method based on mixed integer dynamic optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109713715A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111400641A (en) * | 2019-11-29 | 2020-07-10 | 国网天津市电力公司电力科学研究院 | Day-ahead optimal scheduling method for comprehensive energy system containing heat accumulation type electric heating |
| CN111911988A (en) * | 2020-08-05 | 2020-11-10 | 沈阳华维工程技术有限公司 | Intelligent control method and system for heat storage and release and energy saving of solid heat accumulator |
-
2018
- 2018-12-11 CN CN201811509137.4A patent/CN109713715A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111400641A (en) * | 2019-11-29 | 2020-07-10 | 国网天津市电力公司电力科学研究院 | Day-ahead optimal scheduling method for comprehensive energy system containing heat accumulation type electric heating |
| CN111400641B (en) * | 2019-11-29 | 2024-03-22 | 国网天津市电力公司电力科学研究院 | Day-ahead optimal scheduling method for comprehensive energy system containing regenerative electric heating |
| CN111911988A (en) * | 2020-08-05 | 2020-11-10 | 沈阳华维工程技术有限公司 | Intelligent control method and system for heat storage and release and energy saving of solid heat accumulator |
| CN111911988B (en) * | 2020-08-05 | 2021-07-23 | 沈阳华维工程技术有限公司 | Intelligent control method and system for heat storage and release and energy saving of solid heat accumulator |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109103893B (en) | Auxiliary frequency modulation method for cluster temperature control load participating in power grid AGC | |
| CN107039990B (en) | A kind of multi-heat source coordinated scheduling policy development method improving wind electricity digestion capability | |
| CN108039736A (en) | A kind of large capacity heat accumulation storing up electricity coordinated scheduling method for improving wind-powered electricity generation and receiving ability | |
| CN106602613B (en) | A kind of province ground two-level scheduler section control method for coordinating | |
| CN106160000B (en) | The wind-powered electricity generation that a kind of point of tune-province's tune two-stage is coordinated issues additional control method | |
| CN105337303A (en) | Capacity optimization configuration method for combined heat and power generation micro grid containing heat pump | |
| CN109523065A (en) | A kind of micro- energy net Optimization Scheduling based on improvement quanta particle swarm optimization | |
| CN108092324A (en) | A kind of wind-powered electricity generation participates in the AGC control systems and control method of peak-frequency regulation | |
| CN107332286A (en) | A kind of cogeneration of heat and power containing heat accumulation and wind-powered electricity generation coordinated scheduling method | |
| CN110443401A (en) | A kind of Optimization Scheduling of smart grid | |
| CN111245026A (en) | Virtual power plant regulation and control method, system and equipment | |
| CN106950840A (en) | Towards the integrated energy system layered distribution type control method for coordinating of power network peak clipping | |
| CN102522781A (en) | Method for participating area control error (ACE) control by uniformly modeling wind power plant and thermal power plant | |
| CN111786422B (en) | Real-time optimization scheduling method for participating in upper-layer power grid by micro-power grid based on BP neural network | |
| CN108808737A (en) | Promote the active distribution network Optimization Scheduling of renewable distributed generation resource consumption | |
| CN110829408A (en) | Multi-domain scheduling method considering energy storage power system based on power generation cost constraint | |
| CN112186802A (en) | Multi-time scale rolling scheduling method and system for dynamic economic scheduling | |
| CN110165665A (en) | A kind of source-lotus-storage dispatching method based on improvement multi-objective particle swarm algorithm | |
| CN108494012A (en) | A kind of meter and the electric regional complex energy resource system method for on-line optimization for turning gas technology | |
| CN108390418A (en) | A kind of battery charging and discharging method of AGC scheduling | |
| CN111917140B (en) | Two-layer optimization scheduling model of new energy complementary power generation system | |
| CN107968443A (en) | The AGC control methods that a kind of fiery bundling direct current of scene is sent outside | |
| CN107749645A (en) | A kind of method for controlling high-voltage large-capacity thermal storage heating device | |
| CN109713715A (en) | A kind of hot stored electric heating load control method based on mixed integer dynamic optimization | |
| CN109638857A (en) | Electric heating microgrid dominant eigenvalues layered cooperative based on distributed heat pump group control stabilizes strategy |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20190503 |