CN106446383B - Based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization - Google Patents
Based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization Download PDFInfo
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Abstract
Based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization; construct the double optimization model of a normally opened normal shutdown group of identification; unit in the model hypothesis system is all turned on; then major constraints related with generator output are considered, with the minimum optimization aim of operating cost；According to model solution as a result, for all t=1,2,3 ..., T, if PGitAll meet PGit>PGimin, then No. i-th unit is normally opened unit；If PGitAll meet PGit<δPGimin, then No. i-th unit is normal shutdown group.Rough model is constructed according to constraint condition relevant to start and stop state, filters out Unit Commitment solution space；Specifically include that power-balance constraint, unit ramp loss, minimum start-stop time constraint.It introduces invalid security and constrains identification theory, construct non-effective security constraint identification model.The present invention is giving full play to traditional sequence optimization while advantage in terms of computational efficiency, further enhances the compactedness of algorithm, reduces computing redundancy degree, effectively improves the solution efficiency of algorithm.
Description
Technical field
The invention belongs to electric system and automation research fields, constrain sequence optimization based on improvement more particularly, to a kind of
Uncertain Optimization of Unit Commitment By Improved method for solving.
Background technique
In the case where wind-powered electricity generation accesses background on a large scale, the unit group of the consideration Network Security Constraints of meter and wind power output uncertainty
Closing (Security Constraints Unit Commitment, SCUC) is formulate electric system generation schedule a few days ago main
Foundation.SCUC is mathematically a belt restraining, nonlinear extensive mixed integer programming problem, is an allusion quotation in solution
The difficult problem of the NP (Non-Deterministic Polynomial, NP) of type.With the continuous expansion of power grid scale and intermittently
The extensive access of the renewable energy of property, so that therefore, how fast and effectively the solution difficulty of SCUC problem increasingly increases,
Solve the hot spot for considering that the probabilistic SCUC problem of wind power output has become people's research.
Solution for uncertain SCUC model, current main stream approach are based on being decomposed by Benders, it may be assumed that
It is that Unit Combination and Network Security Constraints check two sub-problems SCUC model decoupling, so first with Benders decomposition method
Recycle afterwards Lagrangian Relaxation, dynamic programming, branch and bound method and various intelligent optimization algorithms to each subproblem into
Row solves.However, since the main advantage of Benders decomposition method is to solve the certainty optimization problem with Complex Constraints,
And advantage is not obvious in terms of corresponding uncertainty optimization problem solving.In recent years, as solution complicated optimum problem
One of effective ways, ordinal optimization theory increasingly cause the concern of people.Sequence is optimized introducing uncertainty SCUC and asked by existing research
Topic solution field abandons seeking its optimal solution from engineering reality, then seeks the solution good enough of SCUC problem, will successfully ask
Solution efficiency improves nearly 30 times on the basis of traditional Benders decomposition method, to demonstrate ordinal optimization theory uncertain
Property SCUC solve field significant advantage.From it is existing research from the point of view of, although sequence optimization be proved to be one kind can be with rapid solving not
The effective ways of certainty SCUC problem, but since traditional ordinal optimization theory is only started with from solution space, by constructing rough model
Model feasible zone scale is reduced, and ignores the rejecting and examination for model redundancy itself, therefore, in solution efficiency side
Face, sequence optimization algorithm still have biggish improvement and room for promotion, therefore need to study a kind of band safety for improving constraint sequence optimization
The uncertain Optimization of Unit Commitment By Improved method for solving of constraint.
Summary of the invention
Quickly and effectively to solve the SCUC problem containing wind-powered electricity generation, the invention proposes a kind of based on improvement constraint sequence optimization
Uncertain Optimization of Unit Commitment By Improved method for solving.Discrete variable recognition strategy is incorporated first in rough model, and is constructed and be based on
The sequence of constraint condition optimizes rough model；Then non-effective security constraint is introduced in accurate model and cuts down strategy, and constructs needle
Accurate model is optimized to the sequence of continuous variable.Compared to conventional needle to the constraint sequence optimization method of SCUC model, the present invention is proposed
Improvement strategy effectively improve the compactedness of sequence optimization algorithm, reduce computing redundancy degree, thus there is higher solutions to imitate
Rate.
The technical scheme adopted by the invention is that:
Based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization, objective function are as follows:
In formula: FGtFor the total operating cost of system, the generating set number in system is M, PGitAnd UGitRespectively indicate i-th
Number active power output and start and stop state of the unit in the t period, UGit=0 expression generating set is in shutdown status, UGit=1 indicates hair
Motor group is in open state.Yit(PGit) be generating set operating cost, Sit(τi) be generator start-up and shut-down costs；
Wherein, the operating cost of generating set, the specific mathematical expression form of start-up and shut-down costs are as follows:
In formula: αi、βi、γiFor unit operating cost parameter.S0i、S1i、τiFor the downtime of i unit, ωiFor start and stop
Cost parameter；
To guarantee power system security reliability service, in the SCUC model of the electric system containing wind-powered electricity generation, decision variable is also needed
Meet following conventional constraint condition:
1) system power Constraints of Equilibrium
In formula: PDtFor system the t period burden with power；PLtFor system the t period transmission of electricity active power loss；PWtFor wind
Output power of the motor group in the t period.
2) generating set power output bound constraint
PGimin≤PGit≤PGimax (5)
In formula: PGiminAnd PGimaxRespectively generating set active power output lower and upper limit.
3) minimum start-stop time constraint
In formula: niFor unit within dispatching cycle maximum allowable start-stop time.
4) unit ramp loss
In formula:WithRespectively unit per hour in active output maximum landslide ability and maximum climbing energy
Power.
5) Network Security Constraints
A·Pt≤Bt (8)
Wherein:
In formula: T is transfer factor matrix；KP、KDRespectively node-generator matrix and node-matrix of loadings；PtWhen for t
The node power matrix at quarter；DtFor the node load matrix of t moment；PLmaxFor transmission capacity on transmission line of electricity L.
Wind power output in Optimization of Unit Commitment By Improved uncertainty is described using chance constraint method, it is positive and negative to construct system respectively
Spinning reserve risk indicator.
Qd≤λ (11)
Qu≤λ (12)
In formula: Qd、QuRespectively positive and negative spinning reserve risk index；λ is spinning reserve risk threshold value, system call department
It converts and obtains after obtaining reliability standard using annual total cost minimum method, usually take between 0~10%.
The specific frame of improvement sequence optimization algorithm proposed by the present invention is as shown in Figure 1, steps are as follows:
Step 1: constructing the double optimization model of a normally opened normal shutdown group of identification, the unit in the model hypothesis system
It is all turned on, considers major constraints related with generator output, then with the minimum optimization aim of operating cost, mathematical modulo
Type are as follows:
According to model solution as a result, for all t=1,2,3 ..., T, if PGitAll meet PGit>PGimin, then i-th
Number unit is normally opened unit；If PGitAll meet PGit<δPGimin, then No. i-th unit is normal shutdown group, and wherein δ is identification ginseng
Number, takes 0.05 herein.
Step 2: rough model being constructed according to constraint condition relevant to start and stop state, filters out Unit Commitment solution space.
Specifically include that power-balance constraint, unit ramp loss, maximum start-stop time constraint, specific mathematical description is as follows, specific to flow
Journey is as shown in Figure 2.
Step 3: introducing invalid security and constrain identification theory, construct non-effective security constraint identification model, specifically include
Following steps:
Step 3.1: by non-effective security constraint is defined as: if after certain constraint is removed, feasible zone in the model with go
Identical before falling, then this is constrained to non-effective constraint.According to definition, it is further proposed that the identification of non-effective security constraint is abundant
Necessary condition are as follows: there is no the vertex that intersection or intersection are only simple body with feasible zone for non-effective constraint.According to upper
Necessary and sufficient condition is stated, construction rejects security constraints A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}Feasible zone Optimized model later
Are as follows:
In formula: ajiThe element of jth row i column in representing matrix A；A-{j}、Bt -{j}Respectively indicate matrix A, B removal jth row
Matrix after element.
Step 3.2: relax to the constraint condition of formula (15), building recognize non-effective security constraint it is abundant it is non-must
Want condition:
Because of Z1≤Z2, so it is believed that working as Z2≤Bj,tWhen, constrain A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}It is non-effective
Constraint.
Step 3.3: analyticity solution being carried out to formula (16), so that proposing can be directly sharp to avoid solving optimization model
The abundant inessential condition of non-effective security constraint is recognized with known parameters, its step are as follows:
(1), assume i1,i2,…,imFor unit sequence, so that
(2), when linear programming problem has feasible solution, then have:
Therefore there are integer k satisfactions:
(3), assume
The then objective function of optimization problem are as follows:
In conclusion the final criterion of non-effective security constraint can be obtained are as follows:
To invalid security constraint recognized and cut down on the basis of, the present invention with SCUC model objective function and with even
Continuous variable PGitBased on relevant constraint condition, construction sequence optimizes accurate model.Its mathematical description is as follows:
Compared with the conventional method, proposed by the present invention to be asked based on the uncertain Optimization of Unit Commitment By Improved for improving constraint sequence optimization
Solution method, has the following advantages and beneficial effects:
(1) although the solution of discrete variable identification model needs the regular hour, since it can reduce sequence optimization slightly
The dimension of rough model solution space, so, as a complete unit, sequence optimization rough model can be promoted by introducing discrete variable Identification Strategy
Computational efficiency.
(2) non-effective security constraint cuts down the tactful non-effective safety that can be recognized in a relatively short period of time more than 90% about
Beam is introduced into sequence optimization accurate model, the computing redundancy degree of accurate model can be effectively reduced, propose computational efficiency.
(3) since improvement strategy improves the compactedness of sequence optimization algorithm, thus improvement random constraints proposed by the present invention
Sequence optimization algorithm has larger promotion compared to traditional sequence optimization algorithm in terms of solution efficiency, and compared to other algorithms, then
Either computational accuracy and efficiency all has a clear superiority.
Detailed description of the invention
Fig. 1 is inventive algorithm general thought block diagram.
Fig. 2 is rough model building flow chart of the present invention.
Fig. 3 is the output of wind electric field curve graph in the embodiment of the present invention.
Fig. 4 is that the security constraint in the embodiment of the present invention cuts down result figure.
Specific embodiment
Based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization, objective function are as follows:
In formula: FGtFor the total operating cost of system, the generating set number in system is M, PGitAnd UGitRespectively indicate i-th
Number active power output and start and stop state of the unit in the t period, UGit=0 expression generating set is in shutdown status, UGit=1 indicates hair
Motor group is in open state.Yit(PGit) be generating set operating cost, Sit(τi) be generator start-up and shut-down costs；
Wherein, the operating cost of generating set, the specific mathematical expression form of start-up and shut-down costs are as follows:
In formula: αi、βi、γiFor unit operating cost parameter.S0i、S1i、τiFor the downtime of i unit, ωiFor start and stop
Cost parameter；
To guarantee power system security reliability service, in the SCUC model of the electric system containing wind-powered electricity generation, decision variable is also needed
Meet following conventional constraint condition:
1) system power Constraints of Equilibrium
In formula: PDtFor system the t period burden with power；PLtFor system the t period transmission of electricity active power loss；PWtFor wind
Output power of the motor group in the t period.
2) generating set power output bound constraint
PGimin≤PGit≤PGimax (5)
In formula: PGiminAnd PGimaxRespectively generating set active power output lower and upper limit.
3) minimum start-stop time constraint
In formula: niFor unit within dispatching cycle maximum allowable start-stop time.
4) unit ramp loss
In formula:WithRespectively unit per hour in active output maximum landslide ability and maximum climbing energy
Power.
5) Network Security Constraints
A·Pt≤Bt (8)
Wherein:
In formula: T is transfer factor matrix；KP、KDRespectively node-generator matrix and node-matrix of loadings；PtWhen for t
The node power matrix at quarter；DtFor the node load matrix of t moment；PLmaxFor transmission capacity on transmission line of electricity L.
Wind power output in Optimization of Unit Commitment By Improved uncertainty is described using chance constraint method, it is positive and negative to construct system respectively
Spinning reserve risk indicator.
Qd≤λ (11)
Qu≤λ (12)
In formula: Qd、QuRespectively positive and negative spinning reserve risk index；λ is spinning reserve risk threshold value, system call department
It converts and obtains after obtaining reliability standard using annual total cost minimum method, usually take between 0~10%.
The specific frame of improvement sequence optimization algorithm proposed by the present invention is as shown in Figure 1, steps are as follows:
Step 1: constructing the double optimization model of a normally opened normal shutdown group of identification, the unit in the model hypothesis system
It is all turned on, considers major constraints related with generator output, then with the minimum optimization aim of operating cost, mathematical modulo
Type are as follows:
According to model solution as a result, for all t=1,2,3 ..., T, if PGitAll meet PGit>PGimin, then i-th
Number unit is normally opened unit；If PGitAll meet PGit<δPGimin, then No. i-th unit is normal shutdown group, and wherein δ is identification ginseng
Number, takes 0.05 herein.
Step 2: rough model being constructed according to constraint condition relevant to start and stop state, filters out Unit Commitment solution space.
Specifically include that power-balance constraint, unit ramp loss, minimum start-stop time constraint, specific mathematical description is as follows, specific to flow
Journey is as shown in Figure 2.
Step 3: introducing invalid security and constrain identification theory, construct non-effective security constraint identification model, specifically include
Following steps:
Step 3.1: by non-effective security constraint is defined as: if after certain constraint is removed, feasible zone in the model with go
Identical before falling, then this is constrained to non-effective constraint.According to definition, it is further proposed that the identification of non-effective security constraint is abundant
Necessary condition are as follows: there is no the vertex that intersection or intersection are only simple body with feasible zone for non-effective constraint.According to upper
Necessary and sufficient condition is stated, construction rejects security constraints A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}Feasible zone Optimized model later
Are as follows:
In formula: ajiThe element of jth row i column in representing matrix A；A-{j}、Bt -{j}Respectively indicate matrix A, B removal jth row
Matrix after element.
Step 3.2: relax to the constraint condition of formula (15), building recognize non-effective security constraint it is abundant it is non-must
Want condition:
Because of Z1≤Z2, so it is believed that working as Z2≤Bj,tWhen, constrain A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}It is non-effective
Constraint.
Step 3.3: analyticity solution being carried out to formula (16), so that proposing can be directly sharp to avoid solving optimization model
The abundant inessential condition of non-effective security constraint is recognized with known parameters, its step are as follows:
(1), assume i1,i2,…,imFor unit sequence, so that
(2), when linear programming problem has feasible solution, then have:
Therefore there are integer k satisfactions:
(3), assume
The then objective function of optimization problem are as follows:
In conclusion the final criterion of non-effective security constraint can be obtained are as follows:
To invalid security constraint recognized and cut down on the basis of, the present invention with SCUC model objective function and with even
Continuous variable PGitBased on relevant constraint condition, construction sequence optimizes accurate model.Its mathematical description is as follows:
Embodiment:
For the present invention by taking the IEEE-118 node power system modified as an example, which includes 54 fired power generating units, 3 wind-force
Generating field, 91 load points, wherein wind power plant is located on node 14,54,95, rated power be respectively 100MW,
200MW, 250MW, active power output are as shown in Figure 3.The stand-by requirement of conventional power unit positive rotation is system peak load in system
8%, negative rotation turns 2% that stand-by requirement is system minimum load, and spinning reserve risk indicator is 0.01.Relevant calculation is in Ying Te
It completes on your -3240 processor 3.40GHz, 4G memory computer of Intel Core i3, is calculated using Matlab 8.0 and 12.5 couples of Cplex
Example is programmed solution.
1), model solution:
Spinning reserve needed for system and each wind power plant that constrained procedure of improving the occasion is sought is as shown in table 1.
The spinning reserve (MW) of each wind power plant of table 1
For the correctness for verifying discrete variable identification model, distinguished using start and stop state of the model to generating set
Know, and its result is compared with tradition based on the final Unit Commitment result that Benders decomposition method acquires, result such as table
Shown in 2.
The comparison of 2 discrete variable identification result of table
By comparing result it is found that in 20 normally opened units, discrete variable identification model of the invention have identified 3
A, discrimination 15%, and in 31 normal shutdown groups, model of the present invention has identified 20, discrimination 64.5%, entirely
There is no the situation of identification mistake occur in identification process.
By the above results comparison it is found that discrete variable identification model proposed by the present invention for often shutdown group discrimination compared with
Height, while also accuracy of identification with higher, rough model can be effectively reduced by incorporating it into sequence optimization rough model
Calculating dimension, promote its solution efficiency.
In order to guarantee accuracy of identification, the identification condition setting of discrete variable identification model of the invention for normally opened unit
It is more harsh, so that the present invention be made to be kept low the discrimination of normally opened unit.And in subsequent calculating process,
Sequence, which optimizes accurate model also and can carry out further optimization to normally opened unit, to be calculated, thus, for normally opened in early period identification model
The lower solving precision that will not influence entire algorithm of the discrimination of unit.
3 rough model performance table of table
As shown in Table 3, after discrete variable recognition strategy being added in sequence optimization rough model, the solution time of rough model
Improve nearly 40s, it is seen then that although solving discrete variable identification model needs time-consuming 2.89s, since which reduce rough models
Whole dimension, so, as a complete unit, the computational efficiency of improved sequence optimization rough model is improved.
The validity that non-effective security constraint cuts down strategy is proposed for the verifying present invention, invalid security constraint is cut
Subtract, remaining security constraint result is as shown in Figure 4 after each period cuts down.
From fig. 4, it can be seen that in IEEE-118 system per period have 372 security constraints, 24 periods share 8928 safety
Constraint condition cuts down model by non-effective security constraint, and 24 periods cut down 8090 invalid security constraints altogether, cut down ratio
Reach 90% or more.The above results show in the security constraint of existing SCUC model there are biggish redundancy, and of the invention
Invalid security constraint cut down strategy can effectively reject above-mentioned redundancy.
From the point of view of each period, the number highest of remaining security constraints 38, minimum 15, and its variation tendency with
Load variations show certain correlation.The reason is that load is heavier, the general safety nargin of system can decline therewith, from
And need to check more routes, that is, have more effective and safe constraints；, whereas if load is lighter, then effective and safe is about
Beam can also be reduced therewith.
In order to verify the validity for introducing invalid security constraint in sequence optimization accurate model and cutting down strategy, it is utilized respectively and draws
Sequence optimization accurate model (after improvement) and traditional sequence optimization accurate model (before improvement) for entering invalid security constraint reduction strategy are right
Each of selected set S start and stop scheme is solved, and runing time is as shown in table 4, it should be pointed out that the emulation is
It is carried out on the basis of improvement sequence optimization rough model proposed by the present invention.
4 accurate model performance table of table
As shown in Table 4, non-effective security constraint of the invention, which cuts down strategy, can quickly identify non-effective security constraint item
Part, entire calculating process only time-consuming 0.09s, and introduce the solution effect of the sequence optimization accurate model after non-effective security constraint is cut down
Rate is improved significantly, and the calculating time reduces nearly 42.43s.It follows that being added in sequence optimization accurate model non-effective
Security constraint cuts down strategy, can effectively promote the computational efficiency of accurate model.
It is excellent using improvement proposed by the present invention constraint sequence for the correctness and validity of the mentioned method for solving of the verifying present invention
Change algorithm and solve SCUC model of the invention, the expense and its conventional power unit start and stop state of optimal solution are as shown in table 5.
5 start and stop scheme of table
For the validity and correctness for comparing method for solving proposed by the present invention, the present invention is also while using following two side
Method solves SCUC problem.
Method one: traditional sequence optimization algorithm.
Method two: the mixed integer programming decomposed based on Benders.
The calculated result of three kinds of methods and calculating time are as shown in table 6.
Each algorithm performance table of table 6
As shown in Table 6, sequence optimization method is either in terms of computational efficiency or solving precision, compared to benders points
Solution method all has obvious advantage.And improvement proposed by the present invention constrains sequence optimization algorithm, optimizes compared to traditional sequence
Method, the total cost cost solved slightly reduce about 0.45%, but in terms of computational efficiency, improve close
18.85%.
Claims (1)
1. based on the uncertain Optimization of Unit Commitment By Improved method for solving for improving constraint sequence optimization, which is characterized in that steps are as follows:
Step 1: constructing the double optimization model of a normally opened normal shutdown group of identification, the unit in the model hypothesis system is whole
It opens, considers major constraints related with generator output, then with the minimum optimization aim of operating cost, mathematical model
Are as follows:
According to model solution as a result, for all t=1,2,3 ..., T, if PGitAll meet PGit>PGimin, then No. i-th machine
Group is normally opened unit；If PGitAll meet PGit<δPGimin, then No. i-th unit is normal shutdown group, and wherein δ is identification parameter,
This takes 0.05；
Step 2: rough model being constructed according to constraint condition relevant to start and stop state, filters out Unit Commitment solution space；Mainly
Include: power-balance constraint, unit ramp loss, maximum start-stop time constraint, specific mathematical description is as follows:
Step 3: introducing invalid security and constrain identification theory, construct non-effective security constraint identification model, specifically include following
Step:
Step 3.1: by non-effective security constraint is defined as: if after certain constraint is removed, feasible zone in the model and before removing
It is identical, then this is constrained to non-effective constraint；According to definition, it is further proposed that abundant necessity of the identification of non-effective security constraint
Condition are as follows: there is no the vertex that intersection or intersection are only simple body with feasible zone for non-effective constraint；It is filled according to above-mentioned
Condition is wanted, construction rejects security constraints A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}Feasible zone Optimized model later are as follows:
In formula: ajiThe element of jth row i column in representing matrix A；A-{j}、Bt -{j}Respectively indicate matrix A, B removal jth row element
Matrix afterwards；
Step 3.2: relaxing to the constraint condition of formula (15), building recognizes the abundant inessential item of non-effective security constraint
Part:
Because of Z1≤Z2, so it is believed that working as Z2≤Bj,tWhen, constrain A{j}(PG1t,PG2t,…PGMt)T≤Bt {j}For non-effective constraint；
Step 3.3: analyticity solution being carried out to formula (16), so that proposing can be to avoid solving optimization model, directly using
Know the abundant inessential condition of the non-effective security constraint of parameter identification, its step are as follows:
(1), assume i1,i2,…,imFor unit sequence, so that
(2), when linear programming problem has feasible solution, then have:
Therefore there are integer k satisfactions:
(3), assume:
The then objective function of optimization problem are as follows:
In conclusion obtaining the final criterion of non-effective security constraint are as follows:
To invalid security constraint recognized and cut down on the basis of, with SCUC model objective function and with continuous variable PGit
Based on relevant constraint condition, construction sequence optimizes accurate model；Its mathematical description is as follows:
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| CN110688725B (en) * | 2019-08-13 | 2022-09-27 | 国网山西省电力公司电力科学研究院 | Robust unit combination method considering operation risk and demand response |
| CN110556823B (en) * | 2019-08-15 | 2023-11-14 | 中国南方电网有限责任公司 | Model dimension reduction based safety constraint unit combination rapid calculation method and system |
| CN111832129A (en) * | 2020-07-01 | 2020-10-27 | 上海交通大学 | Multi-constraint optimization-based mortise rough broaching allowance optimization method |
| CN112966858A (en) * | 2021-02-18 | 2021-06-15 | 云南电网有限责任公司 | Redundancy constraint identification method and system based on variable load |
| CN113420259B (en) * | 2021-06-28 | 2022-09-09 | 广东电网有限责任公司 | Method, device, terminal and medium for reducing combined constraint of safety constraint unit |
| CN113496086B (en) * | 2021-07-09 | 2024-05-28 | 北京清能互联科技有限公司 | Quick calculation method and device for unit combination optimization problem |
| CN116882596B (en) * | 2023-09-07 | 2023-12-15 | 中国地质大学（武汉） | Calculation efficiency improvement method for future random optimization problem of combined heat and power system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102637289A (en) * | 2012-04-16 | 2012-08-15 | 天津大学 | Method for assessing safety value of planning scheme for electrical power system containing large-scale wind power |
| CN104993524A (en) * | 2015-07-17 | 2015-10-21 | 三峡大学 | Wind power-containing electric system dynamic dispatching method based on improved discrete particle swarm optimization |
| CN105225000A (en) * | 2015-09-16 | 2016-01-06 | 三峡大学 | A kind of wind power probability model nonparametric probability method optimized based on Fuzzy Ordered |
| CN105790265A (en) * | 2016-04-21 | 2016-07-20 | 三峡大学 | AC power flow constraint-based uncertainty unit commitment model and solving method |
-
2016
- 2016-09-14 CN CN201610824088.8A patent/CN106446383B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102637289A (en) * | 2012-04-16 | 2012-08-15 | 天津大学 | Method for assessing safety value of planning scheme for electrical power system containing large-scale wind power |
| CN104993524A (en) * | 2015-07-17 | 2015-10-21 | 三峡大学 | Wind power-containing electric system dynamic dispatching method based on improved discrete particle swarm optimization |
| CN105225000A (en) * | 2015-09-16 | 2016-01-06 | 三峡大学 | A kind of wind power probability model nonparametric probability method optimized based on Fuzzy Ordered |
| CN105790265A (en) * | 2016-04-21 | 2016-07-20 | 三峡大学 | AC power flow constraint-based uncertainty unit commitment model and solving method |
Non-Patent Citations (3)
| Title |
|---|
| Stochastic SCUC solution with variable wind energy using constrained ordinal optimization;Hongyu Wu等;《IEEE Transactions on Sustainable Energy》;20131223;第379-388页 * |
| 含风电和储能的电力系统安全约束机组组合问题研究;刘建平 等;《现代电力》;20150803;第48-55页 * |
| 考虑柔性负荷调峰的大规模风电随机优化调度方法;杨楠 等;《电工技术学报》;20131126;第231-238页 * |
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