CN104103023B - Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security - Google Patents
Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security Download PDFInfo
- Publication number
- CN104103023B CN104103023B CN201410385208.XA CN201410385208A CN104103023B CN 104103023 B CN104103023 B CN 104103023B CN 201410385208 A CN201410385208 A CN 201410385208A CN 104103023 B CN104103023 B CN 104103023B
- Authority
- CN
- China
- Prior art keywords
- optimization
- power grid
- power
- potential energy
- elastic potential
- 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.)
- Active
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 66
- 230000005540 biological transmission Effects 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims abstract description 18
- 230000005611 electricity Effects 0.000 title abstract 3
- 238000013507 mapping Methods 0.000 claims abstract description 48
- 238000010248 power generation Methods 0.000 claims abstract description 37
- 238000005381 potential energy Methods 0.000 claims abstract description 34
- 238000004364 calculation method Methods 0.000 abstract description 7
- 238000011160 research Methods 0.000 abstract description 4
- 238000013139 quantization Methods 0.000 abstract 1
- 238000004458 analytical method Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011002 quantification Methods 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 230000002040 relaxant effect Effects 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 208000011580 syndromic disease Diseases 0.000 description 1
- 238000010977 unit operation Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
Due to lack of whole-journey quantization and monotonous security indexes, security is generally taken as an inequation constraint condition in a traditional optimization model, and an optimization target generally only comprises an economical index, so that power grid security can not be simultaneously optimized. Researches find that the mapping elastic potential energy of a power grid can be used as a quantitative index for the power grid security, and the change of the mapping elastic potential energy of the power grid and the network loss change of a major network are in the same trend. The invention provides a comprehensive optimization modeling method for electricity generation and transmission economy and power grid security. The modeling method is characterized in that the mapping elastic potential energy is brought into a target function of an economical power generation optimization model so as to solve the comprehensive optimization problems of the electricity generation and transmission economy and the power grid security. A line reactive constraint condition is omitted so as to improve the calculation efficiency and the reliability of the optimization. The reasonability of the optimization modeling method is verified through an IEEE39 (Institute of Electrical and Electronic Engineers) node system example. The optimization modeling method exhibits practical significance for the optimizing operation and planning of a power system.
Description
Technical Field
And (4) analyzing the safety of the power system (power grid).
Background
The economy of power generation and transmission and the safety of a power grid are simultaneously pursued by the operation of a power system, the former mainly aims to reduce the power generation cost and the transmission loss, and the latter mainly aims to improve the power-angle stability and the voltage stability of the power grid. In the main network, if the reactive compensation capability is strong, the safety mainly refers to the static power-angle stability.
Before, because the problems of whole-course quantification and monotonous indexes of the safety of the power grid cannot be well solved, the safety is generally used as an inequality constraint boundary condition in an optimization model, an optimization target generally only comprises economic indexes, and the safety of the power grid cannot be optimized.
In recent years, in order to solve the comprehensive optimization problem of economy and safety, the optimization model is continuously improved: the first category of thought is to improve security constraints, such as narrowing constraint boundaries to improve security; or after the boundary is flexible and relaxed, the flexibility index and the relaxation variable are put into a target function and are overlapped with the economic index for optimization; the second category of thinking is to keep the original safety constraint boundary, and construct a quantitative safety function to be put into the objective function, such as the line load rate difference sum, the load rate sum of squares, the operation risk index, and the like. The first kind of thinking has the essential problems that the safety cannot be quantified in the whole process, so that the whole process optimization cannot be carried out, and the optimized main body is economic; in addition, simply by softening or relaxing the boundary, the result of the optimization is likely to mutate the "hard" boundary of security. In the second category of thinking, the safety of the whole network is measured from the load rate balance angle, the practical experience is met, but the load rate difference square sum and the load rate square sum have a monotonous relation with the safety, and strict theoretical demonstration is lacked; the adoption of the risk index as the safety index is too complex, the safety within the safety threshold boundary cannot be quantified, and finally, the optimal compromise solution is selected through load rate balance. In addition, the safety inequality constraint in the traditional model also adds difficulty to the optimization solution of the objective function, and the optimization solution has large calculation amount; during random optimization and discrete optimization, the solution result is easy to fall into local optimization.
In terms of network loss optimization, previously, network loss was mainly reduced by reactive power optimization, but the network loss of the main network actually mainly results from active power transmission. Both the network loss and the safety of the power network are related to the distribution of active power flow, but the relationship between the network loss and the safety of the power network is not analyzed. During comprehensive optimization modeling, the relationship between the network loss and the safety is analyzed, and the index component in the objective function is reduced as much as possible, so that the optimization efficiency and the reliability are improved.
Aiming at the defects of the traditional optimization model and aiming at solving the problem of comprehensive optimization of the safety of the power grid and the power generation and transmission economy, the modeling method for the comprehensive optimization of the power generation and transmission economy and the power grid safety is researched based on the earlier-stage research results (Chinese patent: CN102227084B and CN103474993A), namely a mapping elastic grid model of the power grid and a quantitative safety index of the power grid, namely mapping elastic potential energy, the relation between the power transmission economy and the power grid safety is analyzed, and the comprehensive optimization is realized.
Disclosure of Invention
Based on the mapping elastic network model and the mapping elastic potential energy of the power grid, researches find that the mapping elastic potential energy of the power grid not only can be used as a quantitative index of the safety of the power grid, but also changes along with the network loss change of the main network. The modeling method for the comprehensive optimization of power generation and transmission economy and power grid safety brings the mapping elastic potential energy of the power grid into the objective function of the economic power generation optimization model so as to solve the problem of the comprehensive optimization of power generation economy, power transmission economy and power grid safety; the active constraint condition of the line is omitted, so that the calculation efficiency and reliability of optimization are improved. The IEEE39 node system example verifies the rationality of the modeling method. The method has practical significance for optimizing operation and planning of the power system, and based on the optimization model of the method, a power generation operation mode, a power distribution scheme, a power grid wiring planning scheme and the like with the same excellent power generation and transmission economy and power grid safety can be obtained.
Drawings
FIG. 1 Power grid-mapping elastic network topology mapping, (1) Power grid, (2) mapping elastic network
Equivalent mapping elastic branch of fig. 2 power grid
FIG. 3 electric transmission line equivalent model
FIG. 4 IEEE39 node system architecture
FIG. 5 shows the variation trend of the power generation cost, the mapping elastic potential energy and the network loss
Fig. 6 shows a power grid-mapping elastic network structure of power generation system 1
Fig. 7 power grid-mapping elastic network structure of power generation mode 3
Fig. 8 power grid-mapping elastic network structure of power generation mode 5
Detailed Description
1. Mapping elastic potential energy of power grid
Setting node voltage at two ends of AC line LHas a phase difference of thetaLActive power is PLReactance is XLThe resistance is ignored. When U is turnedL1、UL2When the change is not large, the work-angle characteristic of the line L is similar to the stress-deformation characteristic of the elastic branch L with the single degree of freedom, and the mapping relation of L and L state quantities is established as follows
In the above formula, Fl、xlAnd klRespectively the acting force, the elongation and the elastic coefficient of l; k is a radical ofLIs the mapping elastic coefficient of L. Wherein
If the active power of L transmission is expressed as
Then there is
Wherein,according to the physical definition, the elastic potential energy of l is
El＝∫Fldxl(6)
Can obtain the product
According to the mapping relationship, the mapping elastic potential energy of L is
If thetaLSmaller, L can be mapped as a linear elastic branch,is provided with
The elastic net is composed of n branches, and the total potential energy E of the elastic netlΣLinear superposition characteristics are satisfied; mapping elastic potential E of power gridLΣAs well as so. Namely, it is
Wherein: eliPotential energy of the ith branch of the elastic net, ELiAnd mapping the elastic potential energy for the ith branch of the power grid.
The mapping relationship of potential energy is as follows
ELΣ＝ElΣ(13)
2. Relation between power-angle safety of power grid and mapping elastic potential energy
According to the invention patent of 'power grid-elastic mechanics network topology mapping method' (granted publication number: CN102227084B), a power grid is mapped into an elastic network which is vertically stressed, and the association relationship between nodes and branches is kept unchanged, as shown in figure 1. As the branches are all stressed longitudinally, 1 elastic branch can be used for equivalence as long as the total potential energy and the total load are equal, and similarly, the power grid can also be used for equivalence by one branch, as shown in figure 2. The state quantity mapping relation is
Wherein k isleq、kLeqRespectively equivalent elastic coefficient (stiffness) and mapping elastic coefficient, xleq、θLeqRespectively equivalent strain and phase difference, FΣ、PΣThe total load of the elastic network and the total active load of the power grid are respectively.
According to the published patent application "quantitative analysis index of electric network active bearing capacity based on mapping elastic potential energy" (application publication No. CN103474993A), when the total load is fixed, from the perspective of the whole electric network, ELΣApproximately proportional to the equivalent deformation and approximately inversely proportional to the equivalent stiffness; from an internal perspective, when the branch active power distribution is most balanced, then ELΣAt a minimum, i.e. when satisfied
PL1:Pl2:…:PLn＝kL1:kL2:…:kLn(15)
Then ELΣApproximately minimal.
Therefore, the elastic potential energy is mapped, the overall power-angle characteristic of the power grid is quantitatively reflected in the whole process, the actual conditions of the balance and the safety are met, and the elastic potential energy can be used as a quantitative index of the safety of the power grid.
3. Relation between power transmission economy of power transmission network and mapping elastic potential energy
The transmission economy can be measured in terms of grid loss, the smaller the grid loss, the better the transmission economy.
Let the voltage across the line i beTerminal power of PLi+jQLiImpedance of Ri+jXiSusceptance of BiNeglecting conductance, the pi-shaped equivalent model is shown in fig. 3.
Let Δ PiIs the active loss of line i, then
In the main power transmission network, if the reactive compensation is strong and the node voltage amplitude is kept near the reference value, Q is obtainedLi＜＜PLi(ii) a If the conductance is neglected, the line transmission loss is approximated as
So the network loss is approximately
If the phase angles at the two ends of the branch are smaller, the branch can be mapped into a linear elastic branch, and the equations (9) and (18) can be used to obtain
According to the formulas (10) and (19), the compounds are obtained
Wherein, ai＝Ri/Xi. If R of all branches in the gridi/XiA is constant, then
ΔPΣ≈2a·ELΣ(21)
It can be seen that under the condition that the voltage of the grid node is kept good and the phase angles of two ends of the branch are small, ELΣAnd Δ PΣApproximately homodyne.
In addition, the analysis also found: if PΣWhen the active power distribution of the grid satisfies equation (15), Δ PΣApproximately minimal. The following was demonstrated:
construction of the following Lagrange function
Where λ is a constant. Delta PΣThe condition that there is an extremum is that the above formula is paired with PLiHas a partial derivative of zero, i.e.
Therefore it has the advantages of
By substituting the formula (19) into the above formula
If R isi/XiAnd ≈ a, and formula (15) can be obtained from the above formula. I.e., when the formula (15) is satisfied, Δ PΣApproximately an extremum. As can be seen from the actual situation, the minimum value is unique and therefore is approximate to the minimum value. After the syndrome is confirmed.
From the above analysis, it can be seen that if the impedance ratio of the branches in the main network is close, Δ PΣAnd ELΣThe approximate same trend and all tend to be the smallest when the active power distribution is most balanced. Therefore, the active power output of the unit is reasonably distributed, the mapping elastic potential energy of the main network is reduced, and the safety of the power grid and the economy of power transmission can be generally improved at the same time.
4. Traditional optimization model analysis based on security constraints
Heretofore, due to lack of quantitative safety indexes, a traditional optimization model is generally an optimization model based on safety constraints, such as an economic power generation optimization model:
an objective function:
constraint conditions are as follows:
1) power flow equality constraint
2) Upper and lower limit restraint of unit output
PGimin≤PGi≤PGimaxi∈SG(28)
3) And (5) line active power flow upper and lower limit constraint.
Pijmin≤Pij≤Pijmaxi,j∈SL(29)
Wherein: f. ofGTo the total cost of power generation; a isi、bi、ciThe economic parameters of the unit i are obtained; pGiRepresenting the active output of the unit i; jwi shows that nodes i, j are directly connected but i ≠ j; b isiiAnd BijRespectively self-admittance and mutual admittance; pDiIs the load of node i; pGiminAnd PGimaxRespectively the minimum and maximum output limits of the unit; pijminAnd PijmaxMinimum and maximum active limits for line ij, respectively; sB、SLAnd SGIs a node, line and unit set contained in the system.
The main problems of this model are: 1) the security of the power grid cannot be optimized; 2) the network loss cannot be optimized; 3) due to the existence of the intermediate variable inequality constraint condition, the optimization solving calculation amount is large, particularly random optimization is carried out, and the solving result is easy to fall into local minimum.
5. Construction of power generation and transmission economy and safety comprehensive optimization model
And (4) aiming at the target function, besides the traditional economic index of the generator, the mapping elastic potential energy index of the main network is superposed and weighted. Namely, it is
minf＝αfG+β·μ·ELΣ(30)
α and β are weight factors of the two, can be set according to the actual conditions of different power grids, and meet the conditions of α + β -1, 0- α -1, 0- β -1, because fG、ELΣWith different dimensions and orders of magnitude, soLΣMultiplying by the coefficient mu to make the two or the variation range comparable.
The smaller the mapping elastic potential energy in the objective function is, the more balanced the active power of the branch is, and the less the branch is easy to exceed the limit. Therefore, the constraint condition can be simplified to the equations (27) and (28) by omitting the equation (29).
And if the active power of the line exceeds the limit in the optimization result, the initial values of alpha and beta in the model are shown to be not in accordance with the actual requirement of the safety of the power grid. The beta value should be increased and the alpha value should be decreased correspondingly until the alpha and beta values which are in accordance with the actual situation are obtained.
Compared with the traditional power generation economic optimization model, the model also has the optimization functions of power grid safety and power transmission economy and only has 2 target index components. In addition, inequality constraint of line active work is omitted, optimization calculation amount is reduced, and reliability of an optimization result is improved.
6. Example analysis
The IEEE39 node system architecture is shown in fig. 4, where bus 31 is a balanced node and the generator operating economic parameters are shown in table 1.
Based on the power generation cost indicator factor α decrementing,mapping the increasing rule of the elastic potential energy index factor β, setting 5 groups of weight factors of the objective function, wherein mu is 25, obtaining the active output of each unit under 5 power generation modes by adopting a quadratic programming optimization algorithm, as shown in table 2, the power generation cost of each power generation mode and the mapping elastic potential energy ELΣAnd loss Δ PΣEquivalent mapping elastic coefficient kLeqAnd average load rate TAAs shown in table 3. Cost of electric power generation, ELΣAnd Δ PΣThe trend of change of (c) is shown in fig. 5. The mapping elastic potential energy is calculated in MW and radian values, without dimension. The mapping elastic net structures corresponding to the power generation modes 1, 3 and 5 are shown in fig. 6-8.
TABLE 1 economic parameters for unit operation
TABLE 2 active power distribution of units
TABLE 3 Total Power Generation cost, Δ PΣ、ELΣ、kLeq、TA
As can be seen from table 3 and fig. 5, in the objective function of the comprehensive optimization model, if α is larger, the power generation economy is better; if beta is larger, the safety of the power grid and the load balance of the branch circuits are better, and the network loss is lower.
If a power generation economic optimization model is simply adopted, namely the mode 1, the safety and the network loss are the worst of 5 modes; if a safety and power transmission economy optimization model is simply adopted, namely the method 5, the power generation economy is the worst; if the comprehensive optimization model is adopted, as in the mode 3, the power generation cost and the network loss can be reduced, and the safety of the main network can be improved.
As can be seen from fig. 6 to 8, as the β value increases, the overall deformation of the mapping elastic network becomes smaller, the branch load balance becomes better, and the security of the power grid increases. The comprehensive optimization model can play a role in safety optimization.
7. Conclusion
In the optimization operation of the main network, because of the lack of whole-course quantitative safety indexes, the traditional optimization model is generally a power generation economic optimization model based on safety constraints. The main problems of this model are: the safety and the loss of the power grid cannot be optimized; due to the existence of the line power flow inequality constraint condition, the optimization solving calculation amount is large, and the solving result is easy to fall into a local extremum.
Research shows that the mapping elastic potential energy can be used as a quantitative index of the safety of the power grid, and the size change of the mapping elastic potential energy is similar to the change of the grid loss. Therefore, the mapping elastic potential energy is brought into the objective function of the economic power generation optimization model, and the comprehensive optimization problems of power generation, power transmission economy and power grid safety can be solved; the active constraint condition of the line is omitted, and the optimized calculation efficiency and reliability can be improved.
The optimization modeling method has practical significance for optimization operation and planning of the power system, and based on the optimization model of the method, a power generation operation mode, a power distribution scheme, a power grid wiring planning scheme and the like with the same excellent power generation and transmission economy and power grid safety can be obtained.
Claims (1)
1. A modeling method for comprehensive optimization of power generation and transmission economy and power grid safety is characterized by comprising the following steps:
1) in an objective function of the optimization model, mapping elastic potential energy E of the main networkLΣAnd the economic index (i.e. total power generation cost) f of the generatorGWeighted overlap-add, minf α fG+β·μELΣWherein α and β are weight factors, and satisfy α + β -1, 0- α -1, 0- β -1, and the coefficient mu is set according to the actual conditions of different power gridsG、μELΣOf similar order of magnitude;
2) in the step 1), the step (A) is carried out,PGi、ai、bi、cirespectively the active power output and economic parameters of the unit i, ELiMapping elastic potential energy of ith line, ngN is the number of generators and lines respectively;
3) in step 2), the mapping elastic potential energy of the line isWherein P isL、θLAnd kLThe effective voltage of the line, the phase difference of the voltages at two ends and the mapping elastic coefficient,XLis a line reactance;
4) in step 3), if thetaLSmaller, then
5) Because the mapping elastic potential energy in the target function in the step 1) is smaller, the branch active power is more balanced and is more difficult to exceed the limit, the constraint condition omits the upper and lower limit constraints of the active power flow of the line and simplifies the upper and lower limit constraints of the active power flow of the line into the power flow equality constraint and the upper and lower limit constraints of the output of the unit;
6) if the line active power flow is out of limit in the optimization result, it is indicated that the initial values of alpha and beta in the step 1) do not meet the actual requirement of the safety of the power grid, and the beta value should be properly increased and the alpha value should be correspondingly reduced until the alpha and beta values meeting the actual condition are obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410385208.XA CN104103023B (en) | 2014-08-07 | 2014-08-07 | Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410385208.XA CN104103023B (en) | 2014-08-07 | 2014-08-07 | Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104103023A CN104103023A (en) | 2014-10-15 |
| CN104103023B true CN104103023B (en) | 2017-05-10 |
Family
ID=51671154
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410385208.XA Active CN104103023B (en) | 2014-08-07 | 2014-08-07 | Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104103023B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104504620A (en) * | 2015-01-19 | 2015-04-08 | 竺炜 | Safety economic comprehensive optimization based power grid targeting planning design method |
| CN106682758B (en) * | 2016-03-21 | 2020-07-10 | 长沙理工大学 | Power grid targeting planning method for improving active bearing capacity and power transmission economy |
| CN107294082A (en) * | 2016-04-05 | 2017-10-24 | 国家电网公司 | The computational methods of mark post are lost in regional distribution network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103474993A (en) * | 2013-09-02 | 2013-12-25 | 竺炜 | Quantitative analysis index of active bearing capacity of power network based on mapping elastic potential energy |
| CN103475013A (en) * | 2013-09-27 | 2013-12-25 | 清华大学 | Method and system for comprehensively optimizing energy storing power station planning and operating |
| CN103795056A (en) * | 2013-11-07 | 2014-05-14 | 竺炜 | Mapping elastic potential energy-based grid key branch circuit identification method |
-
2014
- 2014-08-07 CN CN201410385208.XA patent/CN104103023B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103474993A (en) * | 2013-09-02 | 2013-12-25 | 竺炜 | Quantitative analysis index of active bearing capacity of power network based on mapping elastic potential energy |
| CN103475013A (en) * | 2013-09-27 | 2013-12-25 | 清华大学 | Method and system for comprehensively optimizing energy storing power station planning and operating |
| CN103795056A (en) * | 2013-11-07 | 2014-05-14 | 竺炜 | Mapping elastic potential energy-based grid key branch circuit identification method |
Non-Patent Citations (4)
| Title |
|---|
| "Contingency constrained economic load dispatch using improved particle swarm optimization for security enhancement";Baskar G 等;《Electric Power System Research》;20091231;第79卷(第4期);第615-621页 * |
| "基于弹性力学空间映射的发电机静态功角稳定分析";竺炜 等;《中国电机工程学报》;20100705;第30卷(第19期);第44-50页 * |
| "基于映射弹性势能的电网有功承载能力定量指标";竺炜 等;《中国电机工程学报》;20140505;第34卷(第13期);第2088-2099页 * |
| "基于自适应聚焦粒子群优化算法的电力系统多目标无功优化";刘述奎 等;《电网技术》;20090705;第33卷(第13期);第48-53页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104103023A (en) | 2014-10-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Azbe et al. | The energy function of a general multimachine system with a unified power flow controller | |
| Beerten et al. | Analysis of power sharing and voltage deviations in droop-controlled DC grids | |
| CN106532711B (en) | Change the Newton load flow calculation method of Jacobian matrix with iteration and node type | |
| Murali et al. | Active and reactive power flow control using FACTS devices | |
| Ng et al. | Ant lion optimizer for optimal reactive power dispatch solution | |
| CN105720594B (en) | The compensation method of pure active three-phase unbalance load compensation capacity | |
| CN104103023B (en) | Comprehensive optimization modeling method for electricity generation and transmission economy and power grid security | |
| Li et al. | Power flow analysis for DC voltage droop controlled DC microgrids | |
| Medina et al. | Reactive power handling by a multi-objective teaching learning optimizer based on decomposition | |
| CN106374513A (en) | Multi-microgrid connection line power optimization method based on leader-follower game | |
| CN104037763A (en) | Rapid decomposing method load flow calculating method suitable for branch system comprising small impedance | |
| Dazahra et al. | Optimal location of SVC using particle swarm optimization and voltage stability indexes | |
| Udgir et al. | Optimal placement and sizing of SVC for voltage security enhancement | |
| Gupta et al. | Static and transient stability enhancement of power system by optimally placing UPFC (Unified Power Flow Controller) | |
| Rathi et al. | Loss minimization with D-FACTS devices using sensitivity based technique | |
| Zhao et al. | Reactive power optimization considering dynamic reactive power reserves | |
| Ramanaiah et al. | Moth flame optimization method for unified power quality conditioner allocation | |
| CN106356867A (en) | Method for determining node reactive voltage sensitivity | |
| Rao et al. | Voltage collapse proximity indicator based placement and sizing of static VAR compensator using bat algorithm to improve power system performance | |
| Dheebika et al. | Optimal location of SVC, TCSC and UPFC devices for voltage stability improvement and reduction of power loss using genetic algorithm | |
| Chaparro et al. | Coordinated tuning of a set of Static Var Compensators using Evolutionary Algorithms | |
| CN104504620A (en) | Safety economic comprehensive optimization based power grid targeting planning design method | |
| Krishnasamy | Optimal location of multiple TCSCs for congestion management | |
| Subramanian et al. | Voltage collapse enhancement and loss reduction by reactive power reserve | |
| Taki et al. | Transient stability improvement using ANFIS controlled UPFC based on energy function |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20170527 Address after: Wanjiali road 410114 in Hunan province Changsha Tianxin District No. 960 2 Patentee after: Changsha University of Sciences and Technology Address before: Wanjiali road 410114 in Hunan province Changsha Yuhua District, 2 No. 960 School of electrical and Information Engineering Changsha University of Science and Technology Patentee before: Du Wei |