CN105279615A - Active power distribution network frame planning method on the basis of bi-level planning - Google Patents
Active power distribution network frame planning method on the basis of bi-level planning Download PDFInfo
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The present invention discloses an active power distribution network frame planning method on the basis of a bi-level planning. The active power distribution network frame planning method on the basis of the bi-level planning comprises the following steps: the first step, bi-level planning model constitution; the second step, gene code; the third step, formation of an original scheme; the fourth step, individual good and bad evaluation; the fifth step, genetic operation; and the sixth step, selection of an optimal scheme. According to the invention, a model is established about an active power distribution network frame planning problem on the basis of a bi-level planning concept, and an improved genetic algorithm is used for solution. Compared with a traditional active power distribution network frame planning method, the bi-level planning concept and the improved genetic algorithm are led in the active power distribution network frame planning method on the basis of a bi-level planning to solve problems. In respect of modeling, the bi-level planning model adopted by the invention is converted to a bi-level planning problem, namely an upper layer planning problem is the construction of a line and the lower layer planning problem is minimum active power output excised amount of distributed generation under the network frame.
Description
technical field
The present invention relates to a kind of distribution system space truss project, specifically a kind of active distribution network space truss project method based on dual layer resist.
Background technology
Dual layer resist is planning and the problem of management with two hierarchical systems, and upper strata decision-making only goes to instruct lower floor decision maker by the decision-making of oneself, the decision-making of not direct intervention lower floor; And lower floor's decision-making using upper strata decision-making as parameter, need make decisions within the scope of the feasible zone of oneself.
Dual layer resist is more complicated than single level programming a lot, is mainly present in three aspects: the nonuniqueness of non-linear, nonconvex property, lower floor's reaction.Dual layer resist has level and levels mutually restricts and combines closely, and feature is as follows:
1) system layer management.Multiple decision maker is in certain hierarchical structure, and upper strata is called leader, and lower floor is called subordinate.
2) each layer decision problem has respective control variable, constraint and target, and these targets are normally inconsistent or conflicting.
3) decision-making of levels has logical order.Lower floor obeys upper strata, and upper strata preferentially makes decisions, and upper strata decision-making as parameter, the basis without prejudice to upper strata decision-making is made lower floor's decision-making by lower floor.
4) levels interacts.The set of strategies of upper strata Decision Making Effect lower floor decision-making, the also realization of some effects lower floor target, upper strata decision-making can not control lower floor's decision-making completely, and in the allowed band of upper strata, there is suitable autonomy in lower floor.Lower floor's decision-making not only realizes own target, also affects the realization of upper strata target.
To the research of dual layer resist, according to the different reflection that lower floor's decision-making obtains upper strata decision-making, two classes can be divided into: lower floor feeds back to the model on upper strata with optimal value and lower floor feeds back to the model on upper strata with optimum solution.The first model, does not require that lower floor plans there is unique optimum solution to each given upper strata decision variable, even if because lower floor's planning has different optimum solutions, but the optimal value of its correspondence is identical, and upper strata planning problem is always determined; Second model, lower floor's planning requirement has unique optimum solution for each upper strata decision variable, if have multiple different solution to certain subplan problem, then upper strata planning problem may be made to become uncertain problem, thus limit the application scope of application of this model.
Active distribution network plan model both related to space truss project, and the optimization relating to again distributed power generation is exerted oneself, and was very easily absorbed in " dimension calamity ".For this reason, the conversion thinking of plan model is proposed: unite planning model conversion is become several subproblem being easy to solve.
Summary of the invention
The present invention is exactly to solve the problems of the prior art, and provides a kind of active distribution network space truss project method based on dual layer resist.
The present invention realizes according to following technical scheme:
Based on an active distribution network space truss project method for dual layer resist, concrete steps are:
The first step, Bi-level Programming Models is formed;
First, the cost of electricity-generating of Computation distribution formula generating, distributed power generation cost of electricity-generating is variable cost, and variable is operation maintenance and fuel cost, can describe by following relational expression:
In formula,
for the unit cost of electricity-generating of distributed power generation,
for the annual electricity generating capacity of distributed power generation,
for the social cost that distributed power generation unit quantity of electricity is avoided, comprise environmental improvement cost etc., reflected by the subsidies granted for policy considerations that government gives;
Objective function can arrange further:
Above formula Section 2, for minimizing cost of electricity-generating and via net loss expense, belongs to optimal power flow problems; Unite planning model can change into a Bilevel Programming Problem, and upper strata planning problem is the construction of line, and lower floor's planning problem then makes the meritorious resection of exerting oneself of distributed power generation minimum under this rack;
Second step, gene code;
For ensureing the connectivity constraint of network, pass through
rank square formation
describe network structure, element be 0,1 two kind of value; The often row of square formation correspond to the load bus of identical numbering; 1st row corresponding power point of square formation, each behavior from the 2nd row is according to the load bus of a definite sequence access network; Namely
in element
represent that the 1st node is connected with power supply point,
represent that the node corresponding to the i-th row is connected with node j;
Encode in this way formed matrix
as follows:
；
3rd step, initial scheme is formed;
Because distributed power generation mainly accesses important load point, most of loads in active distribution network are still powers by connecting bulk power grid, and consider that in the objective function of optimization, circuit investment cost occupies main positions, thus can form the main initial rack scheme considering track investment expense; In order to form comparatively economic rack, can transformer station the shortest to each loaded termination path sum be optimization aim, employing prim minimal spanning tree algorithm solves;
4th step, individual superior and inferior evaluating;
For feasible program, before genetic manipulation, first calculate its fitness, thus select outstanding individuality and carry out chromosomal generation of future generation; Excessive or too small the brought problem of selection in order to avoid penalty function, have chosen the experimental formula of the fitness function considering constraint condition, as follows:
Wherein,
, be mainly used to measure the quality of separating, wherein
for target function value;
for violating the penalty of constraint;
5th step, genetic manipulation;
Take gene mutation and Inter-genic spacer;
6th step, the selection of optimal case;
After carrying out operatings of genetic algorithm, produce multiple feasible scheme, calculate their target function value respectively, the scheme therefrom selecting value minimum is as the rack scheme of active distribution network.
The advantage that the present invention has and good effect are:
The present invention is based on dual layer resist theory and modeling is carried out to active distribution network space truss project problem, Revised genetic algorithum is used to solve, be with the difference of active distribution network space truss project method in the past, the method introduces dual layer resist theory, and introduces Revised genetic algorithum and solve problem.In model is set up, Bi-level Programming Models of the present invention changes into a Bilevel Programming Problem, i.e. the upper strata planning problem construction of line, and lower floor's planning problem then makes the meritorious resection of exerting oneself of distributed power generation minimum under this rack.
Accompanying drawing explanation
Fig. 1 is 29 node power distribution net example geographic position figure of the present invention;
Fig. 2 is the active distribution network space truss project process flow diagram based on dual layer resist of the present invention;
Fig. 3 is Bi-level Programming Models structure of the present invention;
Fig. 4 is meshed network schematic diagram of the present invention;
Fig. 5 is the load timing curve of typical types user of the present invention;
Fig. 6 is the distribution network structure planning chart of consideration active management pattern of the present invention;
Fig. 7 is the distribution network structure planning chart not considering active management pattern of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is elaborated.
Based on an active distribution network space truss project method for dual layer resist, in the research of active distribution network space truss project.With 29 bus test system for embodiment, as shown in Figure 1, the process flow diagram shown in composition graphs 2, is described in detail as follows:
Boundary condition due to active distribution network space truss project problem is that the generating of known distribution formula is layouted the scheme of constant volume, to the construction that rack grows out of nothing.Thus the initial scheme forming rack is needed.Based on this, in order to make genetic algorithm have better performance solving in active distribution network space truss project model, the links such as initial gene coding, initial scheme formation being arranged and improves.Concrete solution procedure is as follows:
The first step, Bi-level Programming Models is formed.
First, the cost of electricity-generating of Computation distribution formula generating.Distributed power generation cost of electricity-generating is variable cost, and variable is operation maintenance and fuel cost, can describe by following relational expression:
In formula,
for the unit cost of electricity-generating of distributed power generation,
for the annual electricity generating capacity of distributed power generation,
for the social cost that distributed power generation unit quantity of electricity is avoided, comprise environmental improvement cost etc., reflected by the subsidies granted for policy considerations that government gives.
Objective function can arrange further:
Above formula Section 2, for minimizing cost of electricity-generating and via net loss expense, belongs to optimal power flow problems (OptimalPowerFlow, OPF).Like this, unite planning model can change into a Bilevel Programming Problem, and upper strata planning problem is the construction of line, and lower floor's planning problem then makes the meritorious resection of exerting oneself of distributed power generation minimum under this rack.Bi-level Programming Models structure after conversion as shown in Figure 3.
Second step, gene code.
For ensureing the connectivity constraint of network, pass through
rank square formation
describe network structure, element be 0,1 two kind of value.The often row of square formation correspond to the load bus of identical numbering.1st row corresponding power point of square formation, each behavior from the 2nd row is according to the load bus of a definite sequence access network.Namely
in element
represent that the 1st node is connected with power supply point,
represent that the node corresponding to the i-th row is connected with node j.
As shown in Figure 4, encode in this way formed matrix
as follows:
3rd step, initial scheme is formed.
Because distributed power generation mainly accesses important load point, most of loads in active distribution network are still powers by connecting bulk power grid, and consider that in the objective function of optimization, circuit investment cost occupies main positions, thus can form the main initial rack scheme considering track investment expense.In order to form comparatively economic rack, can transformer station the shortest to each loaded termination path sum be optimization aim, employing prim minimal spanning tree algorithm solves.
4th step, individual superior and inferior evaluating.
For feasible program, before genetic manipulation, first calculate its fitness, thus select outstanding individuality and carry out chromosomal generation of future generation.Excessive or too small the brought problem of selection in order to avoid penalty function, have chosen the experimental formula of the fitness function considering constraint condition, as follows.
Wherein,
, be mainly used to measure the quality of separating, wherein
for target function value;
for violating the penalty of constraint.
For plan model, constraint condition can meet in solution procedure, and penalty function mainly considers the impact of the out-of-limit and reliability constraint of voltage out-of-limit, Branch Power Flow.Specific as follows:
1) to the penalty of voltage out-of-limit be:
In formula, N is the nodes in power distribution network,
for the scale-up factor of penalty.
2) out-of-limit to Branch Power Flow penalty is:
In formula, m is circuitry number,
for the scale-up factor of penalty.
3) to the ungratified penalty of reliability constraint be:
To sum up,
, can fitness function be determined thus
value, select to carry out heredity and the gene of mutation operation according to fitness value.
5th step, genetic manipulation.
1) gene mutation:
On the basis meeting definitive variation probability, select according to the ratio of ideal adaptation degree, namely roulette is selected.The individuality that adaptive value is high has Fineness gene, and the chance being used to raise up seed is large.In order to ensure to trend towards optimum scheme gradually, the individuality that a certain proportion of adaptive value is low is used to selection and carries out gene mutation, namely completes the conversion between " 0 " " 1 ".
2) Inter-genic spacer:
Comprise single-point to intersect and two point intersection, select two individualities from population, under the prerequisite meeting certain crossover probability, the former is Stochastic choice point of contact, the substring exchanging both sides, point of contact; The latter is Stochastic choice two point of contacts, exchanges the substring between point of contact.
In order to accelerate the speed of convergence of genetic algorithm, have employed the poorest individual correction strategy, being assigned to the poorest individuality by the optimum individual in every generation, thus accelerate the speed of optimum solution acquisition.
6th step, the selection of optimal case.
After carrying out operatings of genetic algorithm, produce multiple feasible scheme, calculate their target function value respectively, the scheme therefrom selecting value minimum is as the rack scheme of active distribution network.
According to upper described method, suppose to complete somewhere load prediction work, wherein the planning plot of 29 nodes needs to form active distribution network, and its location distribution as shown in Figure 1.This network area is 8km2, and electric pressure is 10kV, and the circuit model adopted is pole line LGJ-185.Wherein, 1 node is planned to transformer station, is connected with higher level's electrical network, and demand response project adopts interruptible load project, and the rejection penalty cutting down specific load is 1 yuan/kWh.
(1) correlation parameter of load.
The load parameter in this plot refers to table 1, considers household electricity and commercial power two kinds of load types, in table be classified as the peak load of node, its temporal characteristics is considered according to Fig. 5.
The correlation parameter of table 1 node load
Note: in load type, 1 represents household electricity, and 2 represent commercial power, and 3 represent commercial power;
Significance level is 0.5 and above is considered as important load.
(2) circuit correlation parameter.
For reducing the investment cost of circuit, planning circuit adopts the pole line of LGJ-185, and its correlation parameter is as shown in table 2:
Table 2LGJ-185 line parameter circuit value
(3) correlation parameter of distributed power source.
Said method be when distributed power source capacity and position known carry out, so consider miniature gas turbine, blower fan and photovoltaic generation 3 type in programme.As shown in table 3:
Table 3 study the parameter of distributed power generation
(4) distributed power source arrangement.
Table 4 distributed power source arrangement
(5) other optimum configurations that planning is relevant.
Other optimum configurations of table 5
In sum, adopt the 29 node power distribution net examples containing distributed power generation to verify the active distribution network space truss project method integrating distributed energy and user's request, space truss project conceptual scheme as shown in Figure 6.
In order to be analyzed, probe into the impact of active management pattern on space truss project, to not considering that the space truss project of distributed power generation active management is simulated according to same parameter, programme as shown in Figure 7.
Table 6 program results
Can be drawn to draw a conclusion by table 6 data:
Scheme 1 is consider the programme of active management pattern, and scheme 2 is the programme not considering active management pattern.Fewer than scheme 2 2.3 ten thousand yuan of the rack investment of scheme 1, nearlyer than scheme 2 150,000 yuan of the cost of losses of scheme 1 in addition, this shows that active management pattern can play distributed power generation better and delay positive role in electric grid investment; Power transmission network purchases strategies relatively in, fewer than scheme 2 19.25 ten thousand yuan of scheme 1, this shows that the receiving ability that power distribution network is exerted oneself to distributed power generation under active management pattern is stronger, scheme 1 rack scheme distributed power generation exert oneself more, so the purchases strategies of superior electrical network is less under the program, correspondingly the distributed power generation operation and maintenance cost of scheme 1 is than scheme more than 2.
Fewer than scheme 2 16.68 ten thousand yuan of the environmental protection subsidy of scheme 1, illustrates that the receiving ability of the power distribution network under active management pattern to regenerative resource is stronger.The network year integrated cost of scheme 1 is more excellent than scheme 2 (scheme 1 is 521.24 ten thousand yuan, and scheme 2 is 558.01 ten thousand yuan).This shows that active management pattern is more conducive to playing the positive role that produces for power distribution network of distributed power generation.
Reduction plans rejection penalty in scheme 1 is 15.11 ten thousand yuan fewer than scheme 2 2.24 ten thousand yuan, and the reduction plans amount of scheme 1 is fewer than scheme 2, and the system operation situation under active management pattern is than not considering that active management pattern is more excellent.
Claims (3)
1., based on an active distribution network space truss project method for dual layer resist, concrete steps are:
The first step, Bi-level Programming Models is formed;
First, the cost of electricity-generating of Computation distribution formula generating, distributed power generation cost of electricity-generating is variable cost, and variable is operation maintenance and fuel cost, can describe by following relational expression:
In formula,
for the unit cost of electricity-generating of distributed power generation,
for the annual electricity generating capacity of distributed power generation,
for the social cost that distributed power generation unit quantity of electricity is avoided, comprise environmental improvement cost etc., reflected by the subsidies granted for policy considerations that government gives;
Objective function can arrange further:
Above formula Section 2, for minimizing cost of electricity-generating and via net loss expense, belongs to optimal power flow problems; Unite planning model can change into a Bilevel Programming Problem, and upper strata planning problem is the construction of line, and lower floor's planning problem then makes the meritorious resection of exerting oneself of distributed power generation minimum under this rack;
Second step, gene code;
For ensureing the connectivity constraint of network, pass through
rank square formation
describe network structure, element be 0,1 two kind of value; The often row of square formation correspond to the load bus of identical numbering; 1st row corresponding power point of square formation, each behavior from the 2nd row is according to the load bus of a definite sequence access network; Namely
in element
represent that the 1st node is connected with power supply point,
represent that the node corresponding to the i-th row is connected with node j;
Encode in this way formed matrix
as follows:
；
3rd step, initial scheme is formed;
Because distributed power generation mainly accesses important load point, most of loads in active distribution network are still powers by connecting bulk power grid, and consider that in the objective function of optimization, circuit investment cost occupies main positions, thus can form the main initial rack scheme considering track investment expense; In order to form comparatively economic rack, can transformer station the shortest to each loaded termination path sum be optimization aim, employing prim minimal spanning tree algorithm solves;
4th step, individual superior and inferior evaluating;
For feasible program, before genetic manipulation, first calculate its fitness, thus select outstanding individuality and carry out chromosomal generation of future generation; Excessive or too small the brought problem of selection in order to avoid penalty function, have chosen the experimental formula of the fitness function considering constraint condition, as follows:
Wherein,
, be mainly used to measure the quality of separating, wherein
for target function value;
for violating the penalty of constraint;
5th step, genetic manipulation;
Take gene mutation and Inter-genic spacer;
6th step, the selection of optimal case;
After carrying out operatings of genetic algorithm, produce multiple feasible scheme, calculate their target function value respectively, the scheme therefrom selecting value minimum is as the rack scheme of active distribution network.
2. a kind of active distribution network space truss project method based on dual layer resist according to claim 1, it is characterized in that: in the 4th step, for plan model, constraint condition can meet in solution procedure, penalty function mainly considers the impact of the out-of-limit and reliability constraint of voltage out-of-limit, Branch Power Flow, specific as follows:
To the penalty of voltage out-of-limit be:
In formula, N is the nodes in power distribution network,
for the scale-up factor of penalty;
The penalty out-of-limit to Branch Power Flow is:
In formula, m is circuitry number,
for the scale-up factor of penalty;
To the ungratified penalty of reliability constraint be:
To sum up,
, can fitness function be determined thus
value, select to carry out heredity and the gene of mutation operation according to fitness value.
3. a kind of active distribution network space truss project method based on dual layer resist according to claim 1, is characterized in that: the gene mutation in the 5th step:
On the basis meeting definitive variation probability, select according to the ratio of ideal adaptation degree, namely roulette is selected; The individuality that adaptive value is high has Fineness gene, and the chance being used to raise up seed is large; In order to ensure to trend towards optimum scheme gradually, the individuality that a certain proportion of adaptive value is low is used to selection and carries out gene mutation, namely completes the conversion between " 0 " " 1 ";
Inter-genic spacer in the 5th step:
Comprise single-point to intersect and two point intersection, select two individualities from population, under the prerequisite meeting certain crossover probability, the former is Stochastic choice point of contact, the substring exchanging both sides, point of contact; The latter is Stochastic choice two point of contacts, exchanges the substring between point of contact;
In order to accelerate the speed of convergence of genetic algorithm, have employed the poorest individual correction strategy, being assigned to the poorest individuality by the optimum individual in every generation, thus accelerate the speed of optimum solution acquisition.
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| CN112036611A (en) * | 2020-08-12 | 2020-12-04 | 国网山东省电力公司经济技术研究院 | Power grid optimization planning method considering risks |
| CN113221284A (en) * | 2021-05-21 | 2021-08-06 | 浙江大学 | Double-layer planning solving method and device based on wolf optimizer |
| CN113221284B (en) * | 2021-05-21 | 2022-09-23 | 浙江大学 | Double-layer planning solving method and device based on wolf optimizer |
| CN113904372A (en) * | 2021-10-15 | 2022-01-07 | 华北电力大学 | Active power distribution network multi-objective optimization operation method considering 5G base station access |
| CN113904372B (en) * | 2021-10-15 | 2024-02-27 | 华北电力大学 | Multi-objective optimization operation method of active power distribution network considering 5G base station access |
| CN115470562A (en) * | 2022-09-27 | 2022-12-13 | 清华大学 | Shear wall optimization design method and device based on parameterized model and empirical rule |
| CN115470562B (en) * | 2022-09-27 | 2023-04-25 | 清华大学 | Shear wall optimization design method and device based on parameterized model and experience rules |
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