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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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 invention relates to power distribution system network frame planning, in particular to an active power distribution network frame planning method based on double-layer planning.
Background
The double-layer planning is a planning and management problem with a two-layer system, and an upper-layer decision only guides a lower-layer decision maker through a decision of the upper-layer decision maker and does not directly intervene in the lower-layer decision maker; the lower layer decision needs to take the upper layer decision as a parameter to make a decision within the range of the feasible domain of the lower layer decision.
Two-layer planning is much more complex than single-layer planning, and mainly exists in three aspects: non-linear, non-convex, non-uniqueness of underlying reactions. The double-layer programming has the characteristics of layering, mutual restriction and tight combination of an upper layer and a lower layer, and the characteristics are as follows:
1) and (5) carrying out hierarchical management on the system. Multiple decision makers are in a certain hierarchy, with the upper level called leader and the lower level called slave.
2) Each layer of the decision problem has its own control variables, constraints, and objectives, and these objectives are often inconsistent or contradictory.
3) The decisions of the upper and lower layers have a logical order. The lower layer obeys the upper layer, the upper layer makes a decision preferentially, the lower layer takes the upper layer decision as a parameter, and the lower layer decision is made on the basis of not violating the upper layer decision.
4) The upper and lower layers affect each other. The upper layer decision affects the strategy set of the lower layer decision and also partially affects the realization of the lower layer target, the upper layer decision cannot completely control the lower layer decision, and the lower layer has considerable autonomy within the upper layer allowable range. The lower layer decision not only realizes the self target, but also influences the realization of the upper layer target.
The study of the two-layer programming can be divided into two categories according to the different reflection of the lower-layer decision on the upper-layer decision: the lower layer feeds back the model of the upper layer with an optimal value and the lower layer feeds back the model of the upper layer with an optimal solution. The first model does not require that the lower layer plan have a unique optimal solution for each given upper layer decision variable, because even if the lower layer plan has different optimal solutions, the corresponding optimal values are the same, and the upper layer planning problem is always determined; in the second model, the lower-layer planning requires a unique optimal solution for each upper-layer decision variable, and if a plurality of different solutions exist for a certain sub-planning problem, the upper-layer planning problem can become an uncertainty problem, so that the application range of the model is limited.
The active power distribution network planning model not only relates to network frame planning, but also relates to optimized output of distributed power generation, and is easy to fall into dimension disasters. Therefore, a conversion idea of the planning model is provided: the joint planning model is converted into several sub-problems that are easy to solve.
Disclosure of Invention
The invention provides an active power distribution network frame planning method based on double-layer planning to solve the problems in the prior art.
The invention is realized according to the following technical scheme:
an active power distribution network frame planning method based on double-layer planning specifically comprises the following steps:
step one, forming a double-layer planning model;
first, the power generation cost of distributed power generation is calculated, the power generation cost of distributed power generation is a variable cost, and the variable quantity is the operation maintenance and fuel cost, and can be described by the following relation:
in the formula,for the unit cost of power generation for distributed generation,for the annual energy production of distributed generation,the social cost avoided for the unit electric quantity of distributed power generation, including the environmental governance cost, etc., is reflected by the policy subsidy given by the government;
the objective function may be further organized as:
the second term of the above formula is the minimization of the power generation cost and the network loss cost, and belongs to the optimal power flow problem; the combined planning model can be converted into a double-layer planning problem, the upper layer planning problem is the erection of a line, and the lower layer planning problem is the minimum active power output removal amount of distributed power generation under the grid structure;
secondly, gene coding;
to ensure connectivity constraints of the network, byOrder matrixDescribing a network structure, wherein elements are 0 and 1; each row of the square matrix corresponds to a load node with the same number; the 1 st row of the square matrix corresponds to the power supply point, and the actions from the 2 nd row are fixedSequentially accessing to a load node of a network; namely, it isElement (1) ofIndicating that the 1 st node is connected to the power point,indicating that the node corresponding to the ith row is connected with the node j;
the matrix formed by encoding according to the methodAs follows:
；
thirdly, forming an initial scheme;
because distributed power generation is mainly connected to important load points, most of loads in the active power distribution network are still supplied by being connected with a large power grid, and the line investment cost occupies a main position in consideration of an optimized objective function, an initial grid frame scheme mainly considering the line investment cost can be formed; in order to form a relatively economic network frame, the shortest sum of paths from a transformer substation to each load terminal is taken as an optimization target, and a prim minimum spanning tree algorithm is adopted for solving;
fourthly, evaluating the quality of the individuals;
for a feasible scheme, before genetic manipulation, the fitness of the feasible scheme is calculated, so that excellent individuals are selected for generating chromosomes of the next generation; in order to avoid the problems caused by selecting the penalty function too large or too small, an empirical formula of the fitness function considering the constraint condition is selected, and the formula is as follows:
wherein,mainly for measuring the quality of the solution, whereinIs the objective function value;a penalty function for violating the constraint;
fifthly, genetic manipulation;
adopting gene mutation and gene transposition;
sixthly, selecting an optimal scheme;
after genetic algorithm operation is carried out, a plurality of feasible schemes are generated, the objective function values of the feasible schemes are respectively calculated, and the scheme with the minimum value is selected as the grid structure scheme of the active power distribution network.
The invention has the advantages and positive effects that:
the active power distribution network frame planning method is based on a double-layer planning idea to model the active power distribution network frame planning problem and uses an improved genetic algorithm to solve the problem, and is different from the traditional active power distribution network frame planning method in that the double-layer planning idea is introduced and the improved genetic algorithm is introduced to solve the problem. In the aspect of model establishment, the double-layer planning model adopted by the invention is converted into a double-layer planning problem, namely, an upper-layer planning problem erects a line, and a lower-layer planning problem minimizes the active output removal of distributed power generation under the net rack.
Drawings
FIG. 1 is a diagram of an exemplary geographic location of a 29-node distribution network of the present invention;
FIG. 2 is a flow chart of active power distribution network frame planning based on double-layer planning according to the invention;
FIG. 3 is a two-level planning model structure of the present invention;
FIG. 4 is a schematic diagram of a network of nodes of the present invention;
FIG. 5 is a load timing graph for a typical type of user of the present invention;
FIG. 6 is a grid layout of a power distribution network considering an active management mode according to the present invention;
fig. 7 is a grid layout of a power distribution network without regard to active management mode of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and examples.
An active power distribution network frame planning method based on double-layer planning is used for active power distribution network frame planning research. Taking the 29-node test system as an embodiment, as shown in fig. 1, the following is detailed in conjunction with the flowchart shown in fig. 2:
because the boundary condition of the active power distribution network frame planning problem is the known distributed power generation distribution point constant volume scheme, the network frame is built from nothing to nothing. Thus requiring an initial solution for forming the net mount. Based on the method, in order to enable the genetic algorithm to have better performance in solving the active power distribution network frame planning model, links such as initial gene coding, initial scheme formation and the like are improved. The concrete solving steps are as follows:
the first step is the formation of a two-level planning model.
First, the power generation cost of distributed power generation is calculated. The distributed generation cost of electricity generation is a variable cost, and the variable is the operating maintenance and fuel costs, and can be described by the following relationship:
in the formula,for the unit cost of power generation for distributed generation,for the annual energy production of distributed generation,social costs, including environmental governance costs, avoided for the distributed generation of electricity per unit volume are reflected in government-imposed policy subsidies.
The objective function may be further organized as:
the second term of the above equation is to minimize the power generation cost and the network loss cost, and belongs to the optimal power flow problem (OPF). Therefore, the combined planning model can be converted into a double-layer planning problem, the upper layer planning problem is the erection of the line, and the lower layer planning problem is the minimization of the active output cutting amount of the distributed power generation under the network frame. The structure of the converted two-layer planning model is shown in fig. 3.
Second, gene coding.
To ensure connectivity constraints of the network, byOrder matrixTo describe the network structure, the elements are two values of 0 and 1. Each column of the square matrix corresponds to a load node of the same number. The 1 st row of the square matrix corresponds to a power supply point, and the rows from the 2 nd row are load nodes accessed into the network in a certain sequence. Namely, it isElement (1) ofIndicating that the 1 st node is connected to the power point,indicating that the node corresponding to the ith row is connected with the node j.
The matrix formed by this method is encoded as shown in FIG. 4As follows:
third, the initial protocol is formed.
Because distributed power generation is mainly connected to important load points, most of loads in the active power distribution network are still supplied by being connected with a large power grid, and the line investment cost occupies a main position in consideration of an optimized objective function, so that an initial grid frame scheme mainly considering the line investment cost can be formed. In order to form a relatively economic grid, the shortest sum of paths from the transformer substation to each load terminal is taken as an optimization target, and a prim minimum spanning tree algorithm is adopted for solving.
And fourthly, evaluating the quality of the individuals.
For feasible approaches, prior to genetic manipulation, fitness is calculated to select superior individuals for next generation chromosome generation. To avoid the problems associated with selecting the penalty function too large or too small, an empirical formula is chosen for the fitness function that takes into account the constraints, as shown below.
Wherein,mainly for measuring the quality of the solution, whereinIs the objective function value;a penalty function for violating the constraint.
For a planning model, constraint conditions can be met in the solving process, and the penalty function mainly considers the influence of voltage out-of-limit, branch load flow out-of-limit and reliability constraint. The method comprises the following specific steps:
1) the penalty function for voltage violations is:
in the formula, N is the number of nodes in the power distribution network,is a proportionality coefficient of a penalty function.
2) The penalty function for branch load flow out-of-limit is as follows:
in the formula, m is a branchThe number of the first and second groups is,is a proportionality coefficient of a penalty function.
3) The penalty function for not satisfying the reliability constraint is:
in summary,from which a fitness function can be determinedAnd (3) selecting genes for genetic and mutation operations according to the fitness value.
And fifthly, carrying out genetic operation.
1) Gene mutation:
and on the basis of meeting a certain variation probability, selecting according to the ratio of individual fitness, namely, betting in turn. Individuals with high fitness values have high quality genes and have a high chance of being used to breed offspring. In order to ensure a gradual approach towards optimal solutions, a certain proportion of individuals with low fitness values are selected for the genetic mutation, i.e.the transformation between "0" and "1" is carried out.
2) Gene transposition:
the method comprises single-point crossing and double-point crossing, two individuals are selected from a population, and on the premise of meeting a certain crossing probability, one tangent point is randomly selected in the two individuals, and substrings on two sides of the tangent point are exchanged; the latter is to randomly select two tangents, exchange substrings between the tangents.
In order to accelerate the convergence rate of the genetic algorithm, a worst individual correction strategy is adopted, namely, the optimal individual in each generation is assigned to the worst individual, so that the speed of obtaining the optimal solution is accelerated.
And sixthly, selecting an optimal scheme.
After genetic algorithm operation is carried out, a plurality of feasible schemes are generated, the objective function values of the feasible schemes are respectively calculated, and the scheme with the minimum value is selected as the grid structure scheme of the active power distribution network.
According to the method, assuming that the load prediction work of a certain region is completed, a planning land area with 29 nodes needs to form an active power distribution network, and the geographical position distribution of the active power distribution network is shown in fig. 1. The network area is 8km2, the voltage class is 10kV, and the type of the adopted line is an overhead line LGJ-185. The 1-node planning is a transformer substation and is connected with a superior power grid, the demand response project adopts an interruptible load project, and the penalty cost for reducing unit load is 1 yuan/kWh.
(1) The relevant parameters of the load.
The load parameters of the land are detailed in table 1, two load types of domestic electricity and commercial electricity are considered, the maximum load of the node is listed in the table, and the time sequence characteristic is considered according to the graph of fig. 5.
TABLE 1 parameters associated with node load
Note: in the load types, 1 represents domestic electricity, 2 represents commercial electricity, and 3 represents industrial electricity;
an important load of 0.5 or more is regarded as an important load.
(2) A line-related parameter.
In order to reduce the investment cost of the line, the planned line adopts an overhead line of LGJ-185, and relevant parameters are shown in a table 2:
TABLE 2LGJ-185 LINE PARAMETERS
(3) Relevant parameters of the distributed power supply.
The above method is performed with the distributed power capacity and location known, so 3 types of micro gas turbines, wind turbines and photovoltaic power generation are considered in the planning scheme. As shown in table 3:
TABLE 3 parameters of the distributed Power Generation studied
(4) A distributed power arrangement scheme.
Table 4 distributed power supply arrangement scheme
(5) Planning other parameter settings of interest.
TABLE 5 other parameter settings
In summary, the active power distribution network frame planning method integrating distributed energy and user requirements is verified by using a 29-node power distribution network example with distributed power generation, and a network frame planning scheme diagram is shown in fig. 6.
For comparative analysis, the influence of the active management mode on the grid planning is explored, the grid planning without considering the distributed generation active management is simulated according to the same parameters, and the planning scheme is shown in fig. 7.
TABLE 6 planning results
From the data of table 6, the following conclusions can be drawn:
the scheme 1 is a planning scheme considering the active management mode, and the scheme 2 is a planning scheme not considering the active management mode. The investment of the net rack in the scheme 1 is 2.3 ten thousand yuan less than that in the scheme 2, and in addition, the network loss cost in the scheme 1 is 15 ten thousand yuan less than that in the scheme 2, which shows that the active management mode can better play the positive role of distributed power generation in delaying the investment of a power grid; in the comparison of the power purchase cost of the power transmission network, the scheme 1 is 19.25 ten thousand yuan less than the scheme 2, which shows that the power distribution network in the active management mode has stronger receptivity to distributed power generation output, and the power distribution network in the grid structure scheme of the scheme 1 has more distributed power generation output, so that the power purchase cost to an upper-level power grid in the scheme is lower, and correspondingly, the distributed power generation operation maintenance cost of the scheme 1 is more than that of the scheme 2.
The environmental protection subsidy of the scheme 1 is 16.68 ten thousand yuan less than that of the scheme 2, and the situation shows that the power distribution network in the active management mode has stronger acceptance capability on renewable energy sources. The annual integrated cost of the network of the scheme 1 is better than that of the scheme 2 (the scheme 1 is 521.24 ten thousand yuan, and the scheme 2 is 558.01 ten thousand yuan). Therefore, the active management mode is more beneficial to exerting the positive effect of distributed power generation on the power distribution network.
The penalty cost of load reduction in the scheme 1 is 15.11 ten thousand yuan which is 2.24 ten thousand yuan less than that in the scheme 2, the load reduction amount in the scheme 1 is less than that in the scheme 2, and the system running condition in the active management mode is better than that without considering the active management mode.
Claims (3)
1. An active power distribution network frame planning method based on double-layer planning specifically comprises the following steps:
step one, forming a double-layer planning model;
first, the power generation cost of distributed power generation is calculated, the power generation cost of distributed power generation is a variable cost, and the variable quantity is the operation maintenance and fuel cost, and can be described by the following relation:
in the formula,for the unit cost of power generation for distributed generation,for the annual energy production of distributed generation,the social cost avoided for the unit electric quantity of distributed power generation, including the environmental governance cost, etc., is reflected by the policy subsidy given by the government;
the objective function may be further organized as:
the second term of the above formula is the minimization of the power generation cost and the network loss cost, and belongs to the optimal power flow problem; the combined planning model can be converted into a double-layer planning problem, the upper layer planning problem is the erection of a line, and the lower layer planning problem is the minimum active power output removal amount of distributed power generation under the grid structure;
secondly, gene coding;
to ensure connectivity constraints of the network, byOrder matrixDescribing a network structure, wherein elements are 0 and 1; each row of the square matrix corresponds to a load node with the same number; the 1 st row of the square matrix corresponds to a power supply point, and the rows from the 2 nd row are load nodes accessed into the network in a certain sequence; namely, it isElement (1) ofIndicating that the 1 st node is connected to the power point,indicating that the node corresponding to the ith row is connected with the node j;
the matrix formed by encoding according to the methodAs follows:
；
thirdly, forming an initial scheme;
because distributed power generation is mainly connected to important load points, most of loads in the active power distribution network are still supplied by being connected with a large power grid, and the line investment cost occupies a main position in consideration of an optimized objective function, an initial grid frame scheme mainly considering the line investment cost can be formed; in order to form a relatively economic network frame, the shortest sum of paths from a transformer substation to each load terminal is taken as an optimization target, and a prim minimum spanning tree algorithm is adopted for solving;
fourthly, evaluating the quality of the individuals;
for a feasible scheme, before genetic manipulation, the fitness of the feasible scheme is calculated, so that excellent individuals are selected for generating chromosomes of the next generation; in order to avoid the problems caused by selecting the penalty function too large or too small, an empirical formula of the fitness function considering the constraint condition is selected, and the formula is as follows:
wherein,mainly for measuring the quality of the solution, whereinIs the objective function value;a penalty function for violating the constraint;
fifthly, genetic manipulation;
adopting gene mutation and gene transposition;
sixthly, selecting an optimal scheme;
after genetic algorithm operation is carried out, a plurality of feasible schemes are generated, the objective function values of the feasible schemes are respectively calculated, and the scheme with the minimum value is selected as the grid structure scheme of the active power distribution network.
2. The active power distribution network frame planning method based on the double-layer planning as claimed in claim 1, wherein: in the fourth step, for the planning model, the constraint conditions can be satisfied in the solving process, and the penalty function mainly considers the influence of voltage out-of-limit, branch load flow out-of-limit and reliability constraint, specifically as follows:
the penalty function for voltage violations is:
in the formula, N is the number of nodes in the power distribution network,is the proportionality coefficient of the penalty function;
the penalty function for branch load flow out-of-limit is as follows:
in the formula, m is the number of branches,is the proportionality coefficient of the penalty function;
the penalty function for not satisfying the reliability constraint is:
in summary,from which a fitness function can be determinedAnd (3) selecting genes for genetic and mutation operations according to the fitness value.
3. The active power distribution network frame planning method based on the double-layer planning as claimed in claim 1, wherein: gene mutation in the fifth step:
on the basis of meeting a certain variation probability, selecting according to the ratio of individual fitness, namely, betting in turn; individuals with high fitness values have high-quality genes, and the chance of being used for breeding offspring is high; in order to ensure a gradual optimal scheme, a certain proportion of individuals with low adaptive values are used for selecting gene mutation, namely, the transformation between 0 and 1 is completed;
gene transposition in the fifth step:
the method comprises single-point crossing and double-point crossing, two individuals are selected from a population, and on the premise of meeting a certain crossing probability, one tangent point is randomly selected in the two individuals, and substrings on two sides of the tangent point are exchanged; the latter is to randomly select two tangent points and exchange substrings between the tangent points;
in order to accelerate the convergence rate of the genetic algorithm, a worst individual correction strategy is adopted, namely, the optimal individual in each generation is assigned to the worst individual, so that the speed of obtaining the optimal solution is accelerated.
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