CN107294101A - A kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint - Google Patents
A kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint Download PDFInfo
- Publication number
- CN107294101A CN107294101A CN201710534523.8A CN201710534523A CN107294101A CN 107294101 A CN107294101 A CN 107294101A CN 201710534523 A CN201710534523 A CN 201710534523A CN 107294101 A CN107294101 A CN 107294101A
- Authority
- CN
- China
- Prior art keywords
- unit
- formula
- constraints
- function
- power
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 31
- 238000005457 optimization Methods 0.000 claims abstract description 27
- 238000000205 computational method Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 4
- 230000009194 climbing Effects 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 4
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000009987 spinning Methods 0.000 claims description 3
- 238000005259 measurement Methods 0.000 abstract description 2
- 238000013473 artificial intelligence Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000010200 validation analysis Methods 0.000 description 1
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The present invention relates to a kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint, it is likely to result in being unsatisfactory for the problem of Steady-State Real Power Security Region is constrained for current Unit Combination method, a kind of improved Unit Combination method is proposed, is established with the Model for Multi-Objective Optimization for the security domain measurement index maximum that cost of electricity-generating is minimum and is carried.The present invention is by introducing intermediate variable so that model meets MIXED INTEGER linear optimization problem standard type, and the present invention is using extending ε leash law and optimization software CPLEX is solved to multi-objective problem, acquisition Pareto optimal solution sets.The present invention can coordinate economy and security, more meet actual electric network demand.
Description
Technical field
The present invention relates to the operation of power system, analysis and scheduling field, it is more particularly to a kind of based on security domain target and
The multiple target unit built-up pattern and method for solving of constraint.
Background technology
Unit Combination is the core of generation schedule, determines the start and stop of unit and exerts oneself, and can be for improving
The economy and security of system.Traditional Unit Combination only considered operation constraint, and without Network Security Constraints.In this situation
Under, optimum results may violate Network Security Constraints.With the access of high permeability new energy, the randomness and ripple of new energy
Dynamic property brings more stern challenge to the safety and stability problem of system.
Existing part considers DC power flow, AC power flow or Steady-State Real Power Security Region constraint respectively at present in studying.
Compared with AC and DC trend constraint, security domain can provide current point of operation the distance between to secure border, can be with qualitative assessment
Margin of safety.And it is individually minimum or margin of safety is maximum as single goal using cost of electricity-generating, easily there is scheduling scheme safety
The problem of nargin is small or less economical.And multiple-objection optimization can effectively consider the influence of multiple targets, carried to operations staff
For effective reference.Common multi-objective optimization question processing method is artificial intelligence class algorithm at present or is converted into single goal and asks
Topic is solved.And artificial intelligence class algorithm has the shortcomings that the initial total group of dependence, convergence are unstable, easy to be precocious.Multiple target is turned
Change single integrated target problem to solve, the essence of this kind of method, which remains unchanged, is to solve for single-object problem, it is impossible to obtain Pareto
Optimal solution set.Extension ε leash law has computational efficiency high, it is ensured that the validity of Pareto solutions and can with to solve MIXED INTEGER excellent
The characteristics of business software CPLEX, GUROBI of change problem etc. is effectively combined.But ε leash law is extended in Optimization Problems In Power Systems
In application there is not been reported.In addition, the Optimization Solution such as CPLEX, GUROBI software can fast and efficiently obtain MIXED INTEGER line
The optimal solution of property optimization problem, therefore, by set up model conversion into the form of MIXED INTEGER linear optimization problem be also model
The key set up and solved.
The content of the invention
The invention provides a kind of multiple target unit built-up pattern based on security domain target and constraint.Due to security domain
Contain absolute value in object function expression formula, and be unsatisfactory for the canonical form of mixed integer optimization, the invention provides by drawing
Enter intermediate variable SP1, SP2 so that model meets the method for MIXED INTEGER linear optimization problem canonical form, and utilization extends ε
Leash law and optimization software CPLEX are solved to multi-objective problem.So as to provide a kind of compromise between security and economy
Unit Combination method.
A kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint, it is characterised in that should
Model is based on following object function and constraints：
Described object function is multiple target form：
In formula：Fl(X) it is l-th of object function；X is decision vector；gj(X)、hk(X) it is equation, inequality constraints letter
Number；n1、n2For equality constraint, inequality constraints number；
It is minimum that one of described object function is defined as system cost of electricity-generating, is based on：
P in formulaitFor conventional power unit i period t active power of output；fit(Pit) be conventional power unit i operating cost；
UitIt is conventional power unit i in period t start and stop state, Uit=1 represents operation, Uit=0 represents to shut down,ai、bi、ciFor the coefficient of cost function；SitIt is unit i in t period start-up costs；
The two of the object function are defined as the total nargin maximum of system Steady-State Real Power Security Region, are based on：
In formula：αkiFor the Steady-State Real Power Security Region coefficient determined by system structure parameter；By having for load and branch road k
Work(higher limit is determined；Determined by load and branch road k active lower limit；For in period t, the biography dispatched for branch road k
Defeated Capacity Margin；ηtFor in period t, the branch road transmission capacity nargin of scheduling scheme；η is active static peace in whole dispatching cycle
It is entirely abundant to estimate；B is the set of all branch roads in system；
Described constraints is：
Conventional power unit generates electricity and load power Constraints of Equilibrium：Formula seven；
System spinning reserve capacityFormula eight；
Formula nine；
Unit active power output bound is constrainedFormula ten；
Unit active power ramping rate constraints Di≤Pit-Pi(t-1)≤LiFormula 11；
Minimum start-off time constraintsFormula 12,
Formula 13；
Branch road Steady-State Real Power Security is constrainedFormula 14；
P in formulaLtFor t period predicted load sizes； Respectively unit i upper and lower limit of exerting oneself；Li、DiFor i-th
The climbing upper and lower limit of platform unit；Respectively i-th unit is continuously opened, between the stopping time in the t periods；UTi、DTiRespectively
I-th unit minimum was opened, between the stopping time；Pdmax、PdminRespectively branch road d effective power flow upper and lower limit；cdtWith each section of period t
The load power of point is relevant；
Optimization method specifically includes following steps：
Step 1：The machine unit characteristic data of each generating set of power system are obtained, including active power output bound, are climbed
Slope speed, minimum start-stop time, minimum start/stop time；Day part predicted load size in 24 periods；Network architecture parameters,
Including node and branch road connection, branch road effective power flow bound；And according to load prediction data and network architecture parameters
In calculating formula three
Step 2：By in modelIt is converted into max (SP1,SP2) form；
Step 3：Only consider object function F1, obtain the optimal value F of first aim function1′；Optimize second target letter
Number, and by F1=F1' as constraints, with F2For optimization aim, now F is obtained2Optimal solution F2′；With the first row data
Computational methods are similar, only with F2As object function, the optimal value of now second target function is obtainedFirst is only considered again
Individual object function, and willAs constraints, with F1For optimization aim, now F is obtained1Optimal valuePropped up
Pay table as shown in table 1；
The payoff table of table 1
Step 4：Corresponding object function l maximum in being arranged by payoff table lMinimum valueCalculating is obtained
Object function l span value, computational methods are as follows：
Step 5：Multi-objective optimization question is ultimately converted to one group of single-object problem and solved, method is such as
Under：
s.t.F2-s2=e2Formula 16
Q in formula2By the space-number taken in calculating；s2To increase the slack variable that constraints is introduced newly；
Step 6：Calculated most by one group of single-object problem in CPLEX calculation procedures 5, and using fuzzy set theory
Excellent compromise solution；The corresponding satisfaction of each object function can be represented with fuzzy membership function in each Pareto optimal solutions, most
The Pareto optimal solutions with maximum μ values are defined as into optimal compromise afterwards to solve；Computational methods are as follows：
In above-mentioned a kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint, by formula three
InChange into following form：
The present invention compared with prior art, with advantages below and effect：The present invention establish based on security domain target and
The multiple target unit built-up pattern of constraint, asking for safe region constraint may be unsatisfactory for by overcoming the optimum results of conventional rack combination
Topic.And one group of single-object problem is changed into by extending ε leash law by Model for Multi-Objective Optimization, and problem is changed into
MILP forms, allow it to be solved by MILP business softwares, with good application value and prospect.
Brief description of the drawings
The calculation flow chart of Fig. 1 the inventive method.
Embodiment
Below by embodiment, and data analysis is combined, technical scheme is described in further detail.
Embodiment：
First, the specific method step of the present invention is introduced first：
The model of the present invention is based on following object function and constraints：
Described object function is multiple target form：
In formula：Fl(X) it is l-th of object function；X is decision vector；gj(X)、hk(X) it is equation, inequality constraints letter
Number；n1、n2For equality constraint, inequality constraints number.
It is minimum that one of described object function is defined as system cost of electricity-generating, is based on：
P in formulaitFor conventional power unit i period t active power of output；fit(Pit) be conventional power unit i operating cost；Uit
It is conventional power unit i in period t start and stop state, Uit=1 represents operation, Uit=0 represents to shut down,
ai、bi、ciFor the coefficient of cost function；SitIt is unit i in t period start-up costs.
The two of the object function are defined as system Steady-State Real Power Security Region measurement index maximum, are based on：
In formula：αkiFor the Steady-State Real Power Security Region coefficient determined by system structure parameter；By having for load and branch road k
Work(higher limit is determined；Determined by load and branch road k active lower limit；For in period t, the biography dispatched for branch road k
Defeated Capacity Margin；ηtFor in period t, the branch road transmission capacity nargin of scheduling scheme；η is active static peace in whole dispatching cycle
It is entirely abundant to estimate；B is all set of fingers in system.
Described constraints is：
Conventional power unit generates electricity and load power Constraints of Equilibrium：Formula seven；
System spinning reserve capacityFormula eight；
Formula nine；
Unit active power output bound is constrainedFormula ten；
Unit active power ramping rate constraints Di≤Pit-Pi(t-1)≤LiFormula 11；
Minimum start-off time constraintsFormula 12,
Formula 13；
Branch road Steady-State Real Power Security is constrainedFormula 14.
P in formulaLtFor t period predicted load sizes； Respectively unit i upper and lower limit of exerting oneself；Li、DiFor i-th
The climbing upper and lower limit of platform unit；Respectively i-th unit is continuously opened, between the stopping time in the t periods；UTi、DTiRespectively
I-th unit minimum was opened, between the stopping time；Pdmax、PdminRespectively branch road d effective power flow upper and lower limit；cdtWith each section of period t
The load power of point is relevant.
Specific processing comprises the following steps：
Step 1：Obtain machine unit characteristic data, load prediction data, the network structure of each generating set of power system
Parameter, and according to load prediction data and network architecture parameters calculating parameter；
Step 2：Set up the multiple target unit built-up pattern based on security domain target and constraint, description object function and about
Beam condition；
Step 3：Object function F in the model of foundation2Contain absolute value in expression formula, and be unsatisfactory for mixed integer optimization
Canonical form, by introducing intermediate variable, by model conversion into multiple target Mixed integer linear programming；In step 3
Formula three is calculated containing absolute value, and optimization software CPLEX can not optimize calculating to the object function of this form, by formula threeChange into following form：
Step 4：Optimize each sub-goal by lexicographic order and get paid table；Optimize each by lexicographic order in step 4
The method that sub-goal gets paid table is as follows：
Only consider object function F1, obtain the optimal value F of first aim function1′.Optimize second target function, and will
F1=F1' as constraints, with F2For optimization aim, now F is obtained2Optimal solution F2′.With the computational methods of the first row data
It is similar, only with F2As object function, the optimal value of now second target function is obtainedFirst aim letter is only considered again
Number, and willAs constraints, with F1For optimization aim, now F is obtained1Optimal value
The payoff table of table 1
Step 5：The maximum of corresponding object function l, minimum value can calculate and obtain target letter in being arranged by payoff table l
Number l span value, computational methods are as follows：
Step 6：With (q2- 1) individual mesh point is by object function F2Span value be divided into q2It is individual equidistant.Meter and span
Minimum and maximum value, for F2There is (q2+ 1) individual mesh point.Therefore multi-objective optimization question is eventually converted into as (q2+ 1) individual monocular
Mark optimization problem；In step 6, multi-objective optimization question is ultimately converted to one group of single-object problem and solved, side
Method is as follows：
s.t.F2-s2=e2Formula 19
In formulaRepresent the object function F in payoff table2Maximum and minimum value；riFor object function across
Angle value；q2For the space-number of the 2nd target；s2To increase the slack variable that constraints is introduced newly.
Step 7：With CPLEX to (q2+ 1) individual single-object problem is solved, and obtains final Pareto optimal solutions
Collection.Optimal compromise solution is further asked for using fuzzy set theory.
Need to calculate optimal compromise solution using fuzzy set theory in step 7.Each object function in each Pareto optimal solutions
Corresponding satisfaction can be represented with fuzzy membership function, finally be defined as the Pareto optimal solutions with maximum μ values most
Excellent compromise solution.Computational methods are as follows：
2nd, it is below specific embodiment using the above method.
The application institute's extracting method is verified that as space is limited, the present embodiment is directed to three machines under multiple example models
Exemplified by six node system examples, feasibility and validity to this paper institutes extracting method are analyzed and verified.Concrete condition is as follows：
Validity and superiority that three kinds of plan-validations carry model herein are designed herein.Scheme I：Conventional UC, target is
F1, safe region constraint and target F are not considered2.Scheme II：Target is F2, safe region constraint is not considered.Scheme III：What is carried is more
Objective optimization model.
Scheme II and III consider SR constraints, therefore scheme II and III result fully meet trend constraint.Table 1 gives
The branch road L2-3 of scheme I lower 24 periods effective power flow, branch road L2-3 allows the power maximum passed through to be 100MW.Table 1
As a result show, branch road L2-3 effective power flow is more than limits value in some periods, it means that optimal scheduling scheme I is unsatisfactory for tide
Stream constraint.
The branch road L2-3 of table 1 effective power flow
The cost of electricity-generating and security domain size of three kinds of schemes are compared in table 2.Because scheme I does not consider
Safe region constraint or target, thus scheme I optimum results and be unsatisfactory for security domain constraint.Scheme II target is to greatest extent
Ground improves SR, and with highest operation cost, the cost of electricity-generating than scheme I is high by 10.87%.Scheme III Pareto optimality
Compromise solution have followed trend limitation, and have than scheme II smaller operating cost.
The branch road L2-3 of table 2 effective power flow
It the inventive method can be seen that according to above-mentioned the simulation experiment result can consider in Unit Combination and generate electricity into
This minimum and security domain maximum target, the inventive method can effectively obtain Pareto optimal solution sets, and the inventive method energy
By model conversation into MIXED INTEGER linear optimization form, and it is combined with business optimization software, quickly obtains optimal compromise solution.It is excellent
Change result and show that this method has security and economy concurrently, it is to avoid Unit Combination scheme is unsatisfactory for asking for Steady-State Real Power Security Region
Topic, illustrates that the present invention can meet being actually needed for grid company, has important practical significance and good application prospect.
Claims (2)
1. a kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint, it is characterised in that the mould
Type is based on following object function and constraints：
Described object function is multiple target form：
In formula：Fl(X) it is l-th of object function；X is decision vector；gj(X)、hk(X) it is equation, inequality constraints function；n1、
n2For equality constraint, inequality constraints number；
It is minimum that one of described object function is defined as system cost of electricity-generating, is based on：
P in formulaitFor conventional power unit i period t active power of output；fit(Pit) be conventional power unit i operating cost；UitFor
Conventional power unit i is in period t start and stop state, Uit=1 represents operation, Uit=0 represents to shut down,
ai、bi、ciFor the coefficient of cost function；SitIt is unit i in t period start-up costs；
The two of the object function are defined as the total nargin maximum of system Steady-State Real Power Security Region, are based on：
In formula：αkiFor the Steady-State Real Power Security Region coefficient determined by system structure parameter；By load and branch road k it is active on
Limit value is determined；Determined by load and branch road k active lower limit；In period t, to hold for the transmission that branch road k is dispatched
Measure nargin；ηtFor in period t, the branch road transmission capacity nargin of scheduling scheme；η is that Steady-State Real Power Security is abundant in whole dispatching cycle
Estimate；B is the set of all branch roads in system；
Described constraints is：
Conventional power unit generates electricity and load power Constraints of Equilibrium：
System spinning reserve capacity
Unit active power output bound constrains Pi min≤Pit≤Pi maxFormula ten；Unit active power ramping rate constraints Di≤Pit-
Pi(t-1)≤LiFormula 11；Minimum start-off time constraints
Branch road Steady-State Real Power Security is constrained
P in formulaLtFor t period predicted load sizes；Pi max、Pi minRespectively unit i upper and lower limit of exerting oneself；Li、DiFor i-th
The climbing upper and lower limit of platform unit；Respectively i-th unit is continuously opened, between the stopping time in the t periods；UTi、DTiRespectively
I-th unit minimum was opened, between the stopping time；Pdmax、PdminRespectively branch road d effective power flow upper and lower limit；cdtWith each section of period t
The load power of point is relevant；
Optimization method specifically includes following steps：
Step 1：Obtain the machine unit characteristic data of each generating set of power system, including active power output bound, climbing speed
Rate, minimum start-stop time, minimum start/stop time；Day part predicted load size in 24 periods；Network architecture parameters, including
Node and branch road connection, branch road effective power flow bound；And calculated according to load prediction data and network architecture parameters
In formula three
Step 2：By in modelIt is converted into max (SP1,SP2) form；
Step 3：Only consider object function F1, obtain the optimal value F of first aim function1′；Optimize second target function, and
By F1=F1' as constraints, with F2For optimization aim, now F is obtained2Optimal solution F2′；With the calculating side of the first row data
Method is similar, only with F2As object function, the optimal value of now second target function is obtainedFirst aim is only considered again
Function, and by F2=F2 *As constraints, with F1For optimization aim, now F is obtained1Optimal value F1 *；Get paid table；
Step 4：Corresponding object function l maximum in being arranged by payoff table lMinimum valueCalculating obtains target
Function l span value, computational methods are as follows：rl=Fl max-Fl min；
Step 5：Multi-objective optimization question is ultimately converted to one group of single-object problem and solved, method is as follows：
s.t.F2-s2=e2Formula 16
Q in formula2By the space-number taken in calculating；s2To increase the slack variable that constraints is introduced newly；
Step 6：Optimal folding is calculated by one group of single-object problem in CPLEX calculation procedures 5, and using fuzzy set theory
Middle solution；The corresponding satisfaction of each object function can be represented with fuzzy membership function in each Pareto optimal solutions, finally will
Pareto optimal solutions with maximum μ values are defined as optimal compromise solution；Computational methods are as follows：
2. it is according to claim 1 a kind of based on security domain target and the multiple target unit built-up pattern of constraint and solution side
Method, it is characterised in that by formula threeChange into following form：
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710534523.8A CN107294101B (en) | 2017-07-03 | 2017-07-03 | Multi-target unit combination model based on security domain target and constraint and solving method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710534523.8A CN107294101B (en) | 2017-07-03 | 2017-07-03 | Multi-target unit combination model based on security domain target and constraint and solving method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107294101A true CN107294101A (en) | 2017-10-24 |
| CN107294101B CN107294101B (en) | 2020-01-07 |
Family
ID=60098484
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710534523.8A Active CN107294101B (en) | 2017-07-03 | 2017-07-03 | Multi-target unit combination model based on security domain target and constraint and solving method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107294101B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108879796A (en) * | 2018-08-10 | 2018-11-23 | 广东电网有限责任公司 | Electric power ahead market goes out clear calculation method, system, device and readable storage medium storing program for executing |
| CN110829502A (en) * | 2019-10-17 | 2020-02-21 | 广西电网有限责任公司电力科学研究院 | Multi-target interval power generation scheduling method considering new energy |
| CN111817358A (en) * | 2020-05-29 | 2020-10-23 | 中国电力科学研究院有限公司 | Power transmission network structure optimization method and device considering safety distance constraint |
| CN113410870A (en) * | 2021-07-28 | 2021-09-17 | 大连海事大学 | Power distribution network distributed coordination method based on static security domain |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289566A (en) * | 2011-07-08 | 2011-12-21 | 浙江大学 | Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode |
| CN104362681A (en) * | 2014-11-18 | 2015-02-18 | 湖北省电力勘测设计院 | Island micro-grid capacity optimal-configuration method considering randomness |
-
2017
- 2017-07-03 CN CN201710534523.8A patent/CN107294101B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102289566A (en) * | 2011-07-08 | 2011-12-21 | 浙江大学 | Multiple-time-scale optimized energy dispatching method for micro power grid under independent operation mode |
| CN104362681A (en) * | 2014-11-18 | 2015-02-18 | 湖北省电力勘测设计院 | Island micro-grid capacity optimal-configuration method considering randomness |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108879796A (en) * | 2018-08-10 | 2018-11-23 | 广东电网有限责任公司 | Electric power ahead market goes out clear calculation method, system, device and readable storage medium storing program for executing |
| CN108879796B (en) * | 2018-08-10 | 2021-07-23 | 广东电网有限责任公司 | Electric power day-ahead market clearing calculation method, system, device and readable storage medium |
| CN110829502A (en) * | 2019-10-17 | 2020-02-21 | 广西电网有限责任公司电力科学研究院 | Multi-target interval power generation scheduling method considering new energy |
| CN110829502B (en) * | 2019-10-17 | 2022-06-21 | 广西电网有限责任公司电力科学研究院 | Multi-target interval power generation scheduling method considering new energy |
| CN111817358A (en) * | 2020-05-29 | 2020-10-23 | 中国电力科学研究院有限公司 | Power transmission network structure optimization method and device considering safety distance constraint |
| CN113410870A (en) * | 2021-07-28 | 2021-09-17 | 大连海事大学 | Power distribution network distributed coordination method based on static security domain |
| CN113410870B (en) * | 2021-07-28 | 2023-07-18 | 大连海事大学 | Distributed coordination method for power distribution network based on static security domain |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107294101B (en) | 2020-01-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Peng et al. | Flexible robust optimization dispatch for hybrid wind/photovoltaic/hydro/thermal power system | |
| Huang et al. | Distributed optimal co-multi-microgrids energy management for energy internet | |
| CN105046395B (en) | Method for compiling day-by-day rolling plan of power system containing multiple types of new energy | |
| Shukla et al. | Allocation of optimal distributed generation using GA for minimum system losses in radial distribution networks | |
| Cobos et al. | Robust energy and reserve scheduling under wind uncertainty considering fast-acting generators | |
| Chen et al. | Multi-area economic generation and reserve dispatch considering large-scale integration of wind power | |
| CN107294101A (en) | A kind of multiple target unit built-up pattern and method for solving based on security domain target and constraint | |
| CN106327091A (en) | Multi-region asynchronous coordination dynamic economic dispatching method based on robustness tie line plan | |
| CN107706921B (en) | Micro-grid voltage regulation method and device based on Nash game | |
| CN106253335A (en) | A kind of distributed power source capacity and on-position uncertain distribution network planning method | |
| CN106300336A (en) | A kind of meter and the virtual plant Multiobjective Optimal Operation method of load side and mains side | |
| CN105634024A (en) | Price demand response-based intraday economic scheduling model and linear solving method | |
| CN106253352B (en) | The robust real-time scheduling method of meter and wind-powered electricity generation Probability Characteristics | |
| Huo et al. | Data-driven adaptive operation of soft open points in active distribution networks | |
| CN105528466A (en) | Wind power optimal planning modeling method considering adaptability and economy of power system | |
| CN109858774B (en) | Source network load planning method for improving system safety and coordination | |
| CN108493998A (en) | Consider the robust Transmission Expansion Planning in Electric method of demand response and N-1 forecast failures | |
| CN112016747A (en) | Optimization method suitable for source-load-storage flexible resource overall planning and operation | |
| Rouhi et al. | Unit commitment in power system t by combination of dynamic programming (DP), genetic algorithm (GA) and particle swarm optimization (PSO) | |
| Wang et al. | A novel security stochastic unit commitment for wind-thermal system operation | |
| CN103904664B (en) | A kind of AGC unit real-time scheduling method based on effective static security territory | |
| CN108667077A (en) | A kind of wind storage association system Optimization Scheduling | |
| Fremeth et al. | The role of governance systems and rules in wind energy development: evidence from Minnesota and Texas | |
| CN105305485A (en) | Large-scale intermittent energy consuming security constrained economic dispatch method | |
| CN105226649B (en) | One kind predicting improved provincial power network power generation dispatching optimization method based on bus load |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |