CN107808256A - A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method - Google Patents
A kind of regional high voltage distribution network based on chance constrained programming turns supplier's method Download PDFInfo
- Publication number
- CN107808256A CN107808256A CN201711153906.7A CN201711153906A CN107808256A CN 107808256 A CN107808256 A CN 107808256A CN 201711153906 A CN201711153906 A CN 201711153906A CN 107808256 A CN107808256 A CN 107808256A
- Authority
- CN
- China
- Prior art keywords
- distribution network
- power
- power supply
- voltage distribution
- voltage
- 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 32
- 238000012546 transfer Methods 0.000 claims description 34
- 230000002068 genetic effect Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000005457 optimization Methods 0.000 claims description 7
- 108090000623 proteins and genes Proteins 0.000 claims description 6
- 230000005540 biological transmission Effects 0.000 claims description 4
- 239000006185 dispersion Substances 0.000 claims description 3
- 238000012804 iterative process Methods 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 3
- 230000001131 transforming effect Effects 0.000 abstract description 2
- 230000005611 electricity Effects 0.000 abstract 2
- 240000002853 Nelumbo nucifera Species 0.000 abstract 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 abstract 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 abstract 1
- 206010061876 Obstruction Diseases 0.000 abstract 1
- 230000035699 permeability Effects 0.000 abstract 1
- 238000010586 diagram Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 3
- 230000009466 transformation Effects 0.000 description 3
- 238000010521 absorption reaction Methods 0.000 description 2
- 230000000903 blocking effect Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 101150060629 def gene Proteins 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000002349 favourable effect Effects 0.000 description 1
- 238000010353 genetic engineering Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000002356 single layer Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
- G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Development Economics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a kind of regional high voltage distribution network based on chance constrained programming to turn supplier's method, topology reconstruction ability based on high voltage distribution network, it is proposed that the high voltage distribution network chance constraint using the feasible topology status of 110kV electric transforming unit groups as control object turns to supply model, multimode modeling is carried out to stochastic variable using the probability density function of wind-powered electricity generation load error, target is distributed as with source lotus power equalization, builds the high-voltage fence running optimizatin technology using topology reconstruction as means under condition of uncertainty.The present invention can effectively dredge the consumption contradiction of regional high-voltage fence after the non-homogeneous access of high permeability wind-powered electricity generation, and the flexible utilization efficiency for helping to lift high-voltage fence assets is to stabilize obstruction risk.
Description
Technical Field
The invention belongs to the technical field of power system automation, and particularly relates to a design of a regional high-voltage distribution network power supply transfer method based on opportunity constraint planning.
Background
After the national energy agency publishes national New energy [2011]226 No. Notification about development of distributed access wind power and national New energy [2011]374 No. Notification about development and construction guidance of distributed access wind power projects ], medium-scale wind power is directly merged into regional power grids for rapid development of local absorption technology. Due to the limitation of randomness and fluctuation of wind energy resources, wind power access is non-uniform, and local consumption of regional power grids is difficult. Meanwhile, the regional power grid construction is difficult to ensure the adaptability to the increase of each local load, and the local region is easy to have heavy load. Therefore, the high-voltage power grid in the high-permeability area is easy to have the coexistence situation of blocking risk and consumption predicament. In order to ensure power supply reliability, a plurality of standby supply lines are often built in a regional high-voltage power distribution network, so that potential power supply paths of the regional high-voltage power distribution network are extremely large, the coexistence of a looped network and a radial network is realized, and the balanced distribution of power can be realized by utilizing the characteristic of high flexibility and transferability of the regional high-voltage power distribution network, but the conventional research on the transfer operation of a Distributed Generation (DG) in the power distribution network mainly focuses on a 10kV medium-voltage level. Due to the lack of a clear model of the high voltage distribution network considering DG, existing research is mainly focused on a single layer, such as the problem of consumption brought by only reconstructing the high voltage distribution network or only by DG. Meanwhile, due to the randomness of wind power, a power transfer algorithm under a deterministic condition is not completely applicable, and a medium-scale wind power deterministic representation method of a high-voltage distribution network is lacked in the prior art. In conclusion, the uncertainty of wind power control is difficult to be controlled by the prior art and the model, and the consumption difficulty and the load overload of the regional high-voltage distribution network cannot be considered.
Disclosure of Invention
The invention aims to solve the defect that a high-voltage distribution network in a high-permeability area is difficult to take into account of consumption difficulty and load overload under medium-scale wind power access, and provides a regional high-voltage distribution network power supply method based on opportunity constraint planning.
The technical scheme of the invention is as follows: a regional high-voltage distribution network supply method based on opportunity constraint planning comprises the following steps:
s1, constructing a high-voltage distribution network opportunity constraint transfer model.
S2, searching a feasible topological structure of the power supply unit group in the high-voltage distribution network opportunity constraint transfer model to obtain a feasible topological state of the power supply unit group.
The rule for searching the feasible topological structure of the power supply unit group is as follows:
the action of the circuit breaker needs to be matched in any switching operation, namely the number of the circuit breakers in a closed state is constant;
and for the condition that a plurality of 220kV transformer substations are selected in the transfer process, only one transfer direction can be selected.
And S3, solving the opportunity constraint transfer supply model of the high-voltage power distribution network by adopting a decimal genetic algorithm according to the feasible topological state of the power supply unit group, and realizing load dispersion and wind power consumption optimization of the regional high-voltage power distribution network after the high-permeability wind power is accessed in a non-uniform manner.
Step S3 specifically includes the following sub-steps:
and S31, generating an initial population of the decimal genetic algorithm according to the feasible topological state of the power supply unit group.
And S32, calculating an objective function of the high-voltage distribution network opportunity constraint transfer model, and calculating the individual fitness of the current population.
And S33, judging whether an algorithm termination condition is met (the iteration times reach the preset maximum iteration times), if so, entering a step S34, and otherwise, entering a step S36.
And S34, outputting the feasible topological state of the power supply unit group as an algorithm result.
And S35, judging whether the algorithm result meets the opportunity constraint condition, if so, ending the solving, otherwise, returning to the step S31.
And S36, judging whether the iteration process falls into local optimum or not (when the iteration number h is greater than a preset iteration number threshold value K, the optimum fitness of continuous n iterations is the fitness of the same individual in the population), if so, entering a step S37, and otherwise, entering a step S38.
And S37, calculating the optimal individual fitness according to the individual fitness of the current population, and entering the step S38.
S38, generating the next generation of individuals by genetic operation, and returning to the step S32.
The beneficial effects of the invention are: the invention provides a high-voltage distribution network opportunity constraint transfer model taking a feasible topological state of a 110kV power transformation unit group as a control object based on the topological reconstruction capability of a high-voltage distribution network, performs multi-state modeling on random variables by using a probability density function of wind power-load errors, and constructs a high-voltage power network operation optimization technology taking topological reconstruction as a means under an uncertain condition by taking source load power balanced distribution as a target. The method can effectively relieve the consumption contradiction of the high-voltage power grid in the area after the high-permeability wind power is accessed in the non-uniform mode, and is favorable for improving the flexible utilization efficiency of the high-voltage power grid assets to stabilize the blocking risk.
Drawings
Fig. 1 is a flowchart of a regional high-voltage distribution network power supply method based on opportunity constraint planning according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a regional high-voltage power grid transformation unit and a connection relationship thereof according to an embodiment of the present invention.
Fig. 3 is a schematic representation diagram of a topology structure of a regional high-voltage power grid according to an embodiment of the present invention.
Fig. 4 is a flowchart illustrating a substep of step S3 according to an embodiment of the present invention.
Fig. 5 is a local system wiring diagram of a regional high-voltage power grid according to an embodiment of the present invention.
Fig. 6 is a schematic diagram illustrating a ratio of a wind power and load power certainty prediction value provided by an embodiment of the present invention.
Fig. 7 is a schematic diagram illustrating comparison of system states before and after optimization according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It is to be understood that the embodiments shown and described in the drawings are merely exemplary and are intended to illustrate the principles and spirit of the invention, not to limit the scope of the invention.
The embodiment of the invention provides a regional high-voltage distribution network transfer method based on opportunity constraint planning, which comprises the following steps S1-S3 as shown in figure 1:
s1, constructing a high-voltage distribution network opportunity constraint transfer model.
Based on the topology reconstruction capability of the high-voltage distribution network, the invention provides an opportunity constraint transfer model of the high-voltage distribution network, which takes a 110kV power supply unit as a control object. The high-voltage distribution network transfer operation is realized by changing the connection relation among all power supply units, so the meaning of the power supply units and the connection relation among the power supply units are firstly clarified by the model. The 110kV substation is usually equipped with 2-3 two-coil transformers, which are generally a single bus section, an inner bridge and a T-connection combination of the single bus section and the inner bridge and a line transformer set, as shown in fig. 2. In order to ensure the power supply reliability, a large number of standby supply lines are built in the 110kV network of the regional high-voltage power grid according to the design standard of a ring network, the longest standby supply distance can reach 10 kilometers, at least 1 standby supply line exists in each 110kV transformer substation, and the topological structure expression of the regional power grid high-voltage power grid is shown in fig. 3.
S2, searching a feasible topological structure of the power supply unit group in the high-voltage distribution network opportunity constraint transfer model to obtain a feasible topological state of the power supply unit group.
Aiming at power supply units in the high-voltage distribution network opportunity constraint transfer model, the power supply units are divided into groups according to the following rules:
(1) Any two power supply units with electrical connection belong to the same power supply unit group a as well as all power supply units between them if there is no 220kV substation in this connection.
(2) The unit group is supplied with power by fixed 220kV substations, which are in direct electrical contact with a certain power supply unit of the unit group (without passing through other intra-group units).
(3) The 220kV transformer substation can simultaneously supply power to a plurality of unit groups, and the load size of the transformer substation is equal to the power of the power supply unit carried at the moment.
The power supply unit switching operation is realized through a 110kV network topological state, the state of a 110kV line breaker is also changed, the feasible topological state of the power supply unit group is determined in advance by utilizing a search algorithm, the algorithm efficiency can be improved, and the rule is as follows:
(1) The action of the circuit breaker needs to be matched in any switching operation, namely the number of the circuit breakers in a closed state is constant.
(2) And for the situation that a plurality of 220kV transformer substations are selected in the transfer process, only one transfer direction can be selected.
And S3, solving the opportunity constraint transfer supply model of the high-voltage power distribution network by adopting a decimal genetic algorithm according to the feasible topological state of the power supply unit group, and realizing load dispersion and wind power consumption optimization of the regional high-voltage power distribution network after the high-permeability wind power is accessed in a non-uniform manner.
As shown in fig. 4, step S3 specifically includes the following substeps S31-S38:
s31, coding genes according to the feasible topological state of the power supply unit group to generate an initial population of the decimal genetic algorithm, wherein the calculation formula is as follows:
wherein x i Representing a feasible topological state of the ith power supply unit group, h i Represents the controllable DG power adjustment of the ith power supply unit group, P i ENS Indicating the load shedding of the ith power supply unit group, B Gro For a set of power supply unit groups, g i Indicates the gene corresponding to the ith power supply unit group, G t Represents the entire set of coding genes at time t.
And S32, calculating an objective function of the high-voltage distribution network opportunity constraint transfer model, and calculating the individual fitness of the current population.
The objective function f is calculated as:
f=min(f 1 +a 1 f 2 +a 2 f 3 ) (2)
wherein f is 1 Area power grid power equilibrium distribution coefficient expected value, f under opportunity constraint planning 2 For the number of times the circuit breaker can be operated during the switching operation, f 3 For the adjustment of the controllable DG power, i.e. for the adjustment of the controllable DG power in local areas during the supply process to improve the wind power absorption capacity and balance the load, a 1 、a 2 Is the penalty factor of the corresponding operation.
The formula (2) ensures that the action times of the circuit breaker and the controllable DG power are adjusted within a safety threshold value, so that the network power distribution is balanced, the wind power consumption capability is improved, and the heavy load of a transformer substation is eliminated.
f 1 The calculation formula of (2) is as follows:
f 1 =exp{||k n -k avg || 2 },n=1,2,...,N T (3)
wherein k is n Represents the load factor, k, of the nth 220kV substation avg Represents the mean load factor, N, of a 220kV transformer substation T The number of the transformer substations is 220 kV.
Formula (3) represents the load factor k of each 220kV transformer substation n About its mean value k avg 2-norm of f 1 The smaller the value is, the more balanced the system power distribution under the opportunity constraint planning is, and the phenomena of consumption contradiction and transformer substation overloading are effectively solved. k is a radical of formula n The calculation formula of (c) is:
whereinThe load power and the fan of the power supply unit of the nth 220kV transformer substation in the ith power supply unit group at the moment t are respectivelyThe power of the electric motor is controlled by the power controller,rated capacity of the nth 220kV transformer substation, T is a time set, phi is a 220kV transformer substation set, B Gro Is a power supply unit group set.
f 2 The calculation formula of (2) is as follows:
whereinRespectively the topological states before and after the optimization of the jth operable circuit breaker at the moment t under the ith feasible topology,for a feasible set of topologies, B Bro Is a set of operational circuit breakers.
f 3 The calculation formula of (2) is as follows:
wherein h is i,j Represents the controllable DG power adjustment of the jth transforming unit in the ith power supply unit group, B Bro For a set of operable circuit breakers, B Uni Is a power transformation unit set.
The current population individual fitness fit (d) is calculated by the following formula:
wherein N is d And d is the position of the individual in the population determined according to the size of the objective function, sp is the selection pressure, and sp is more than or equal to 1 and less than or equal to 2 in the embodiment of the invention.
And S33, judging whether an algorithm termination condition is met, if so, entering a step S34, and otherwise, entering a step S36.
In the embodiment of the invention, the algorithm termination condition is as follows: the iteration times reach the preset maximum iteration times.
And S34, outputting the feasible topological state of the power supply unit group as an algorithm result.
And S35, judging whether the algorithm result meets the opportunity constraint condition, if so, ending the solving, otherwise, returning to the step S31.
Opportunity constraints include node voltage constraints, load rate constraints, and wind power constraints.
The formula of the node voltage constraint condition is as follows:
equation (8) is the voltage amplitude chance constraint of the 220kV node and the 110kV node, whereinRespectively represent the ith 110kV node voltage and the jth 220kV node voltage,respectively a lower limit and an upper limit of the voltage amplitude of the 110kV node,the lower limit and the upper limit of the voltage amplitude of the 220kV node are respectively, alpha is a confidence level, and Pr {. Is a reliability function.
The formula of the load rate constraint is:
equation (9) is the 220kV substation and 220kV line load rate opportunity constraint, where k n Is the load factor, k, of the nth 220kV transformer substation n,max Is changed into 220kVUpper limit of the plant load factor, k l Load factor, k, of the first 220kV line l,max The load factor of the 220kV line is the upper limit.
The formula of the wind power constraint condition is as follows:
whereinRespectively the load active power and the wind power active power of the nth 220kV transformer substation at the time t,load reactive power and wind power reactive power of the nth 220kV transformer substation at the time t are respectively,and the value is the threshold value of the wind power active power reverse transmission quantity, T is a time set, and phi is a 220kV transformer substation set.
Equation (10) represents wind power active power constraints, in generalThat is, the reverse transmission amount of the 110kV wind power active power to the 220kV main network is allowed to be within a certain threshold, and the formula (11) represents wind power reactive power constraint, that is, the reverse transmission of the 110kV wind power reactive power to the 220kV main network is not allowed.
And S36, judging whether the iteration process falls into local optimum or not, if so, entering a step S37, and otherwise, entering a step S38.
In the embodiment of the invention, the conditions for the iteration process to fall into the local optimum are as follows: and when the iteration times h are larger than a preset iteration time threshold value K, the optimal fitness of the continuous n iterations is the fitness of the same individual in the population.
And S37, calculating the optimal individual fitness according to the individual fitness of the current population, and entering the step S38.
The decimal genetic algorithm optimizing range adopted by the embodiment of the invention is the product of feasible topological states in each unit group, and the scale is large, so that the local optimization is easy to fall into in the iteration process. For this case, the individual fitness fit (d) is dynamically adjusted: i.e. the number of iterations h&And K, if the fact that the local optimum is possibly detected is detected, the optimum individual fitness is adjusted, and the optimum individual fitness is fit m (d) The calculation formula of (2) is as follows:
whereinAnd f, in order to adjust the coefficient of the fitness, fit (d) is the individual fitness of the current population, h is the iteration number, and K is a preset iteration number threshold.
S38, generating a next generation individual by genetic operation, and returning to the step S32.
In the embodiment of the invention, the specific method for generating the next generation of individuals by using genetic manipulation is as follows:
sorting the individuals of the generation according to the individual fitness (if the population consists of 50 individuals, sorting serial numbers are 1-50), removing 2 individuals with the minimum fitness (1-48 is remained), copying 2 individuals (1, 2) with the maximum fitness to directly enter the next generation, dividing 1-48 into 24 pairs, carrying out specific position exchange genes between each pair (for example, aec and dbf are new individuals after the second stage of abc and def gene exchange), and forming the next generation of 50 individuals by newly generated 48 individuals and the previous generation of 1 and 2 individuals.
The feasibility and the effectiveness of the regional high-voltage distribution network power supply transfer method based on opportunity constraint planning provided by the invention are verified by a regional power grid local system:
as shown in fig. 5, the 220kV system has 6 substations and 10 lines; the 110kV system has 38 power supply units and 40 lines, wherein 9 lines are in a standby state, and the system is divided into 6 power supply unit groups (A1-A6) according to the regional high-voltage distribution network power supply transferring method provided by the embodiment of the invention. The installed capacity of a 17-node wind power plant is 150MW, the installed capacity of a 27-node wind power plant is 50MW, and the installed capacity of a 38-node wind power plant is 100MW; nodes 29 and 37 are connected with 8MW controllable DGs.
The power and the load power of each wind power plant in the future 2 hours are shown in the table 1 by taking 30min as a time interval.
TABLE 1
Under the initial working condition, all industrial loads of the A3 belt are overloaded, and the load rate is 120 percent; at the moment, the wind speed is high, the output of the 38-node wind power plant is close to the rated power and cannot be consumed on site, and the wind power is returned in A6, so that a large transfer space exists in the network. The obtained results are shown in fig. 6 and 7, and it can be known from the figures that the load ratios of the optimized 220kV transformer substations are relatively balanced, the overload lines return to the rated capacity, and the transformer substations have no heavy load phenomenon and no wind power consumption dilemma. Table 2 shows the operation of each high-voltage circuit breaker in each time period.
TABLE 2
It will be appreciated by those of ordinary skill in the art that the embodiments described herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited embodiments and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. A regional high-voltage distribution network transfer method based on opportunity constraint planning is characterized by comprising the following steps:
s1, constructing a high-voltage distribution network opportunity constraint transfer model;
s2, searching a feasible topological structure of a power supply unit group in the high-voltage distribution network opportunity constraint transfer model to obtain a feasible topological state of the power supply unit group;
and S3, solving the opportunity constraint transfer supply model of the high-voltage power distribution network by adopting a decimal genetic algorithm according to the feasible topological state of the power supply unit group, and realizing load dispersion and wind power consumption optimization of the regional high-voltage power distribution network after the high-permeability wind power is accessed in a non-uniform manner.
2. The regional high-voltage distribution network power distribution method according to claim 1, wherein the rule for searching the feasible topological structure of the power supply unit group in step S2 is as follows:
the action of the circuit breaker needs to be matched in any switching operation, namely the number of the circuit breakers in a closed state is constant;
and for the situation that a plurality of 220kV transformer substations are selected in the transfer process, only one transfer direction can be selected.
3. The regional high-voltage distribution network transfer method according to claim 1, wherein the step S3 specifically comprises the following sub-steps:
s31, generating an initial population of the decimal genetic algorithm according to the feasible topological state of the power supply unit group;
s32, calculating a target function of the high-voltage distribution network opportunity constraint transfer model, and calculating the individual fitness of the current population;
s33, judging whether an algorithm termination condition is met, if so, entering a step S34, and otherwise, entering a step S36;
s34, outputting the feasible topological state of the power supply unit group as an algorithm result;
s35, judging whether the algorithm result meets an opportunity constraint condition, if so, ending the solving, otherwise, returning to the step S31;
s36, judging whether the iteration process falls into local optimum or not, if so, entering a step S37, and otherwise, entering a step S38;
s37, calculating the optimal individual fitness according to the individual fitness of the current population, and entering the step S38;
s38, generating the next generation of individuals by genetic operation, and returning to the step S32.
4. The regional high-voltage distribution network power supply method according to claim 3, wherein the calculation formula for generating the decimal genetic algorithm initial population in step S31 is as follows:
wherein x i Represents the feasible topological state of the ith power supply unit group, h i Represents the controllable DG power adjustment of the ith power supply unit group, P i ENS Indicating the load shedding of the ith power supply unit group, B Gro For a set of power supply units, g i Indicates the gene corresponding to the i-th power supply unit group, G t Represents the total set of coding genes at time t.
5. The method according to claim 3, wherein the objective function f in step S32 is calculated by the following formula:
f=min(f 1 +a 1 f 2 +a 2 f 3 )
wherein f is 1 Area power grid power equilibrium distribution coefficient expected value, f under opportunity constraint planning 2 For the number of times the circuit breaker can be operated during the switching operation, f 3 For controllable DG power scaling, a 1 、a 2 Is the penalty factor of the corresponding operation.
6. The method for regional high-voltage distribution network transfer according to claim 3, wherein the calculation formula of the individual fitness fit (d) of the current population in the step S32 is as follows:
wherein N is d And d is the size of the population, d is the position of the individual in the population determined according to the size of the objective function, and sp is the selection pressure.
7. The regional high-voltage distribution network transfer method according to claim 3, wherein the algorithm termination condition in step S33 is: the iteration times reach the preset maximum iteration times.
8. The regional high-voltage distribution network trans-supply method according to claim 3, wherein the opportunity constraint conditions in the step S35 include a node voltage constraint condition, a load rate constraint condition and a wind power constraint condition;
the formula of the node voltage constraint condition is as follows:
wherein V i 110 、Respectively represent the ith 110kV node voltage and the jth 220kV node voltage,respectively a lower limit and an upper limit of the voltage amplitude of the 110kV node,respectively setting a lower limit and an upper limit of a voltage amplitude of a 220kV node, wherein alpha is a confidence level, and Pr {. Is a reliability function;
the formula of the load rate constraint condition is as follows:
wherein k is n Is the load factor, k, of the nth 220kV transformer substation n,max Is the upper limit of the load factor k of the 220kV transformer substation l Is the load factor, k, of the l 220kV line l,max The load factor upper limit of the 220kV line is set;
the formula of the wind power constraint condition is as follows:
whereinRespectively the load active power and the wind power active power of the nth 220kV transformer substation at the time t,load reactive power and wind power reactive power of the nth 220kV transformer substation at the time t are respectively,the threshold value of the wind power active power reverse transmission quantity is shown, T is a time set, and phi is a 220kV transformer substation set.
9. The regional high-voltage distribution network transfer method according to claim 3, wherein the conditions for the iterative process to fall into the local optimum in step S36 are: and when the iteration times h are larger than a preset iteration time threshold value K, the optimal fitness of the continuous n iterations is the fitness of the same individual in the population.
10. The regional high-voltage distribution network power supply method according to claim 3Characterized in that the optimal individual fitness fit in the step S37 m (d) The calculation formula of (2) is as follows:
whereinAnd f, in order to adjust the coefficient of the fitness, fit (d) is the individual fitness of the current population, h is the iteration number, and K is a preset iteration number threshold.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711153906.7A CN107808256B (en) | 2017-11-20 | 2017-11-20 | Regional high-voltage distribution network supply transfer method based on opportunity constraint planning |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201711153906.7A CN107808256B (en) | 2017-11-20 | 2017-11-20 | Regional high-voltage distribution network supply transfer method based on opportunity constraint planning |
Publications (2)
Publication Number | Publication Date |
---|---|
CN107808256A true CN107808256A (en) | 2018-03-16 |
CN107808256B CN107808256B (en) | 2021-03-23 |
Family
ID=61580336
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201711153906.7A Expired - Fee Related CN107808256B (en) | 2017-11-20 | 2017-11-20 | Regional high-voltage distribution network supply transfer method based on opportunity constraint planning |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN107808256B (en) |
Cited By (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107767094A (en) * | 2017-09-22 | 2018-03-06 | 北京京东尚科信息技术有限公司 | A kind of logistics ladder freight charges optimization method and device |
CN108964037A (en) * | 2018-07-19 | 2018-12-07 | 四川大学 | Based on the reconstitution model of high voltage distribution network |
CN111008769A (en) * | 2019-11-26 | 2020-04-14 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
CN112952844A (en) * | 2021-05-14 | 2021-06-11 | 国网浙江省电力有限公司金华供电公司 | Load adjusting method under power grid out-of-limit working condition |
Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2015125643A (en) * | 2013-12-26 | 2015-07-06 | 川崎重工業株式会社 | Facility planning method, program and device for distributed energy system |
CN104810863A (en) * | 2015-05-11 | 2015-07-29 | 东南大学 | Generator set active power real-time dispatching method considering wind power prediction error |
CN104868465A (en) * | 2014-02-26 | 2015-08-26 | 云南电力调度控制中心 | Power system grid structure reconfiguration and optimization method based on fuzzy chance constraint |
CN104866921A (en) * | 2015-05-22 | 2015-08-26 | 天津大学 | Power distribution system network reconstruction method based on safety domain |
-
2017
- 2017-11-20 CN CN201711153906.7A patent/CN107808256B/en not_active Expired - Fee Related
Patent Citations (4)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JP2015125643A (en) * | 2013-12-26 | 2015-07-06 | 川崎重工業株式会社 | Facility planning method, program and device for distributed energy system |
CN104868465A (en) * | 2014-02-26 | 2015-08-26 | 云南电力调度控制中心 | Power system grid structure reconfiguration and optimization method based on fuzzy chance constraint |
CN104810863A (en) * | 2015-05-11 | 2015-07-29 | 东南大学 | Generator set active power real-time dispatching method considering wind power prediction error |
CN104866921A (en) * | 2015-05-22 | 2015-08-26 | 天津大学 | Power distribution system network reconstruction method based on safety domain |
Non-Patent Citations (4)
Title |
---|
刘继栋: "基于十进制编码的差分进化算法在配电网重构中的应用", 《电力自动化设备》 * |
张曦等: "基于高压配电网变电单元分组重构的城市电网输电阻塞管控模型及算法", 《中国电机工程学报》 * |
王威等: "基于机会约束规划的含风机配电网重构", 《电系***及其自动化学报》 * |
白牧可等: "基于机会约束规划的DG与配电网架多目标协调规划", 《电工技术学报》 * |
Cited By (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN107767094A (en) * | 2017-09-22 | 2018-03-06 | 北京京东尚科信息技术有限公司 | A kind of logistics ladder freight charges optimization method and device |
CN108964037A (en) * | 2018-07-19 | 2018-12-07 | 四川大学 | Based on the reconstitution model of high voltage distribution network |
CN108964037B (en) * | 2018-07-19 | 2020-04-07 | 四川大学 | Construction method based on high-voltage distribution network reconstructability model |
CN111008769A (en) * | 2019-11-26 | 2020-04-14 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
CN111008769B (en) * | 2019-11-26 | 2022-09-06 | 国电南瑞科技股份有限公司 | Energy transformation optimization method and system considering power blockage |
CN112952844A (en) * | 2021-05-14 | 2021-06-11 | 国网浙江省电力有限公司金华供电公司 | Load adjusting method under power grid out-of-limit working condition |
Also Published As
Publication number | Publication date |
---|---|
CN107808256B (en) | 2021-03-23 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Li et al. | Distributed adaptive robust voltage/var control with network partition in active distribution networks | |
Niknam et al. | Scenario-based multiobjective distribution feeder reconfiguration considering wind power using adaptive modified particle swarm optimisation | |
Maulik et al. | Optimal operation of droop-controlled islanded microgrids | |
Ganguly et al. | Distributed generation allocation on radial distribution networks under uncertainties of load and generation using genetic algorithm | |
Millar et al. | Impact of MV connected microgrids on MV distribution planning | |
CN104578157B (en) | Load flow calculation method of distributed power supply connection power grid | |
CN107808256B (en) | Regional high-voltage distribution network supply transfer method based on opportunity constraint planning | |
Erlich et al. | Optimal dispatch of reactive sources in wind farms | |
Xu et al. | Multi-objective robust optimization of active distribution networks considering uncertainties of photovoltaic | |
CN104037765B (en) | The method of active power distribution network service restoration scheme is chosen based on improved adaptive GA-IAGA | |
Roustaee et al. | Multi-objective stochastic operation of multi-microgrids constrained to system reliability and clean energy based on energy management system | |
Zhao et al. | Distributed risk-limiting load restoration for wind power penetrated bulk system | |
CN112018823B (en) | Multi-objective robust optimization method for power distribution network | |
Soltani et al. | Stochastic multiobjective distribution systems phase balancing considering distributed energy resources | |
Askarzadeh | Solving electrical power system problems by harmony search: a review | |
Xu et al. | Stochastic multi-objective optimization of photovoltaics integrated three-phase distribution network based on dynamic scenarios | |
Ramaswamy et al. | Relevance of voltage control, grid reconfiguration and adaptive protection in smart grids and genetic algorithm as an optimization tool in achieving their control objectives | |
Li et al. | Active distribution network active and reactive power coordinated dispatching method based on discrete monkey algorithm | |
Foltyn et al. | OPF solution for a real Czech urban meshed distribution network using a genetic algorithm | |
Moger et al. | Fuzzy logic approach for reactive power coordination in grid connected wind farms to improve steady state voltage stability | |
Zarei et al. | Multi‐objective optimization model for distribution network reconfiguration in the presence of distributed generations | |
Gao et al. | Multi-objective dynamic reconfiguration for urban distribution network considering multi-level switching modes | |
Boroumandfar et al. | Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty | |
Liu et al. | Two-stage optimal economic scheduling for commercial building multi-energy system through internet of things | |
Elgamal et al. | A framework for profit maximization in a grid-connected microgrid with hybrid resources using a novel rule base-bat algorithm |
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 | ||
CF01 | Termination of patent right due to non-payment of annual fee | ||
CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20210323 Termination date: 20211120 |