CN112766532A - DG planning method based on improved mixed integer differential evolution algorithm - Google Patents

DG planning method based on improved mixed integer differential evolution algorithm Download PDF

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CN112766532A
CN112766532A CN202010999265.2A CN202010999265A CN112766532A CN 112766532 A CN112766532 A CN 112766532A CN 202010999265 A CN202010999265 A CN 202010999265A CN 112766532 A CN112766532 A CN 112766532A
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王海宾
魏弋然
陈疆
张竹青
高亮
陈德高
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State Grid Xinjiang Electric Power Co Ltd Urumqi Power Supply Co
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Abstract

The invention discloses a DG planning method based on an improved mixed integer differential evolution algorithm, which comprises the following steps: establishing a line loss minimum objective function of power distribution network reconstruction; establishing constraint conditions of power distribution network reconstruction, including power flow constraint, voltage constraint, DG power constraint and branch power constraint; and solving by adopting an improved mixed integer differential evolution algorithm to obtain the DG configuration of the power distribution network which meets the constraint condition and has the minimum line loss. The method and the system can reasonably plan the DG capacity accessed to the power distribution network, so that the power distribution network frame is more reasonably planned under the condition of accessing a large number of DGs.

Description

DG planning method based on improved mixed integer differential evolution algorithm
Technical Field
The invention relates to distributed energy (DG) capacity planning of an access distribution network, in particular to a DG planning method based on an improved mixed integer differential evolution algorithm.
Background
In recent years, with the gradual depletion of conventional energy sources, particularly petroleum, coal and natural gas, on the earth and the growing attention on the global problem of environmental protection and energy conservation, and the continuous development of power science and technology, the Distributed Generation (DG) technology mainly based on renewable energy sources such as wind power Generation and photovoltaic power Generation is becoming mature and becoming a hot point of research at home and abroad. However, the access of a large number of DG to the distribution network will change the structure and operation control manner of the conventional distribution network greatly. The research on reconstruction and fault recovery of the power distribution network containing multiple DGs is less, and especially, the technical method how to reasonably plan the island effect of the DGs is less in ensuring the power supply reliability of important loads and ensuring the economic operation, so that the reasonable planning of the DG capacity is very important and practical.
Disclosure of Invention
Aiming at the problems in the prior art, the invention provides a DG planning method based on an improved mixed integer differential evolution algorithm, which can reasonably plan the DG capacity accessed to a power distribution network, so that the power distribution network frame planning is more reasonable under the condition of accessing a large amount of DGs.
The purpose of the invention is realized by the following technical scheme.
The DG planning method based on the improved mixed integer differential evolution algorithm comprises the following processes:
the method comprises the following steps: establishing a line loss minimum objective function of power distribution network reconstruction;
step two: establishing constraint conditions of power distribution network reconstruction, including power flow constraint, voltage constraint, DG power constraint and branch power constraint;
step three: and solving by adopting an improved mixed integer differential evolution algorithm to obtain the DG configuration of the power distribution network which meets the constraint condition and has the minimum line loss.
In the first step, the line loss minimum objective function is:
Figure BDA0002693685260000021
in the formula: r isijIs the resistance of branch i-j; pij、QijRespectively the active power and the reactive power flowing through each time period at the tail ends of the branches i-j; vijIs the node voltage at the end of branch i-j; n is the number of nodes in the power distribution network; k is a radical ofijThe state variable of the switch for branch i-j is
Figure BDA0002693685260000022
Discrete amount, 0 for open, 1 for closed; min F is the minimum function of line loss.
And step two, calculating the constraint condition of the power distribution network reconstruction according to the following formula:
and (3) power flow constraint:
Figure BDA0002693685260000023
Figure BDA0002693685260000024
in the formula: pi、QiRespectively input active power and reactive power of a node i; pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDi、QDiRespectively the active power and the reactive power of the load at the node i; u shapei、UjVoltages of nodes i and j, respectively; gij、BijAnd deltaijRespectively the conductance, susceptance and phase angle difference of the branches i-j; n is the number of nodes in the power distribution network;
voltage constraint:
Figure BDA0002693685260000025
in the formula: n is the number of nodes in the power distribution network; u shapei,minAnd Ui,maxRespectively representing the lower limit and the upper limit of the allowable voltage of the node i; i isijIs the current of branch i-j; i isij,maxThe upper limit of the current for branch i-j;
DG power constraint:
Figure BDA0002693685260000026
in the formula: pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDG,max、PDG,minThe maximum value and the minimum value of the active power of the DG are respectively; qDG,max、QDG,minMaximum and minimum reactive power of DG respectively;
branch power constraint:
Figure BDA0002693685260000031
in the formula:
Figure BDA0002693685260000032
respectively the power and maximum power of the branches i-j.
The solving process by adopting the improved mixed integer differential evolution algorithm in the third step is as follows:
step 1: initializing parameter population size NP (total number of individuals), wherein each individual is a D-dimensional vector, a scaling factor F and a cross factor CR;
step 2: randomly generating an initial population P in a decision space;
step 3: calculating the fitness of each individual, namely an objective function value;
step 4: randomly selecting three different individuals
Figure BDA0002693685260000033
G is an algebra;
step 5: variation of individuals in a population:
Figure BDA0002693685260000034
Vi Gis an individual after mutation;
step 6: crossing individuals in the population;
step 7: and (3) selecting the individuals in the population: when new vector individuals
Figure BDA0002693685260000035
Is more than the target vector individual
Figure BDA0002693685260000036
When better, reserve
Figure BDA0002693685260000037
To the next generation population, otherwise target vector individuals
Figure BDA0002693685260000038
Still remain in the population, once again as the next generation parent vector;
step 8: and if the maximum iteration number is reached or the error requirement is met, obtaining the optimal DG configuration result, and if not, returning to Step 3.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method for planning the DGs based on the improved mixed integer differential evolution algorithm promotes the economic operation of the power distribution network under the condition that a large amount of DGs are accessed into the power distribution network, and ensures that the planning configuration of the DGs is more reasonable and more adaptive.
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Fig. 1 is a topological structure diagram of a 10kV power distribution network.
Detailed Description
For a better understanding of the present invention, reference is made to the following description taken in conjunction with the accompanying drawings and examples.
The DG planning method based on the improved mixed integer differential evolution algorithm comprises the following processes:
the method comprises the following steps: establishing a line loss minimum objective function of power distribution network reconstruction, as follows:
Figure BDA0002693685260000041
in the formula: r isijIs the resistance of branch i-j; pij、QijRespectively the active power and the reactive power flowing through each time period at the tail ends of the branches i-j; vijIs the node voltage at the end of branch i-j; n is the number of nodes in the power distribution network; k is a radical ofijThe state variable of the switch for branch i-j is
Figure BDA0002693685260000045
Discrete amount, 0 for open, 1 for closed; minF is a minimum function of line loss.
Step two: and establishing constraint conditions of power distribution network reconstruction, including power flow constraint, voltage constraint, DG power constraint and branch power constraint.
The power flow constraint is calculated according to the following formula:
Figure BDA0002693685260000042
Figure BDA0002693685260000043
in the formula: pi、QiRespectively input active power and reactive power of a node i; pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDi、QDiRespectively the active power and the reactive power of the load at the node i; u shapei、UjVoltages of nodes i and j, respectively; gij、BijAnd deltaijRespectively the conductance, susceptance and phase angle difference of the branches i-j; and n is the number of nodes in the power distribution network.
The voltage constraint is calculated as follows:
Figure BDA0002693685260000044
in the formula: n is the number of nodes in the power distribution network; u shapei,minAnd Ui,maxRespectively representing the lower limit and the upper limit of the allowable voltage of the node i; i isijIs the current of branch i-j; i isij,maxThe upper limit for the current in branch i-j.
The DG power constraint is calculated as follows:
Figure BDA0002693685260000051
in the formula: pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDG,max、PDG,minThe maximum value and the minimum value of the active power of the DG are respectively; qDG,max、QDG,minThe maximum and minimum values of the reactive power of DG, respectively.
The branch power constraint is calculated according to the following formula:
Figure BDA0002693685260000052
in the formula:
Figure BDA0002693685260000053
respectively the power and maximum power of the branches i-j.
Step three: and solving by adopting an improved mixed integer differential evolution algorithm to obtain the DG configuration of the power distribution network which meets the constraint condition and has the minimum line loss.
Solving using the modified mixed integer differential evolution algorithm generally follows the following steps:
step 1: initializing parameter population size NP (total number of individuals), wherein each individual is a D-dimensional vector, a scaling factor F and a cross factor CR;
step 2: randomly generating an initial population P in a decision space;
step 3: calculating the fitness of each individual, namely an objective function value;
step 4: randomly selecting three different individuals
Figure BDA0002693685260000054
G is an algebra;
step 5: variation of individuals in a population:
Figure BDA0002693685260000055
Vi Gis an individual after mutation;
step 6: crossing individuals in the population;
step 7: and (3) selecting the individuals in the population: when new vector individuals
Figure BDA0002693685260000056
Is more than the target vector individual
Figure BDA0002693685260000057
When better, reserve
Figure BDA0002693685260000058
To the next generation population, otherwise target vector individuals
Figure BDA0002693685260000059
Still remain in the population, once again as the next generation parent vector;
step 8: and if the maximum iteration number is reached or the error requirement is met, obtaining the optimal DG configuration result, and if not, returning to Step 3.
Example (b):
the calculation example takes an IEEE 33 node system as an example, network reconstruction calculation is carried out by using the DG planning method based on the improved mixed integer differential evolution algorithm and taking the minimum line loss as an objective function, and meanwhile, the lowest node voltage when the line loss is minimum is output. The method comprises the following steps:
the method comprises the following steps: and establishing a reconstructed line loss minimum objective function of the power distribution network, as shown in formula (1).
Step two: and establishing constraint conditions for power distribution network reconstruction, as shown in the formulas (2) to (6).
Step three: the specific solving process of the improved differential evolution algorithm comprises the following steps:
step 1: parameters population size NP (total number of individuals) are initialized, each individual being a D-dimensional vector, a scaling factor F, a crossover factor CR. (ii) a
Step 2: randomly generating an initial population P in a decision space;
step 3: calculating the fitness of each individual, namely an objective function value;
step 4: randomly selecting three different individuals
Figure BDA0002693685260000061
G is an algebra;
step 5: variation of individuals in a population:
Figure BDA0002693685260000062
Vi Gis an individual after mutation;
step 6: crossing individuals in the population;
step 7: and (3) selecting the individuals in the population: when new vector individuals
Figure BDA0002693685260000063
Is more than the target vector individual
Figure BDA0002693685260000064
When better, reserve
Figure BDA0002693685260000065
To the next generation population, otherwise target vector individuals
Figure BDA0002693685260000066
Still remain in the population, once again as the next generation parent vector;
step 8: if the maximum iteration number is reached or the error requirement is met, obtaining the optimal DG configuration result, otherwise, returning to Step 3: .
The power distribution network is provided with 33 nodes and 37 branches, wherein 5 of the nodes are interconnection switches, the rated voltage is 12.66kV, the total load is 5048.26kVA + j2547.32kVar, and the topological structure of the power distribution network is shown in figure 1.The DG installation location and capacity are shown in table 1. Initial grid loss without distributed generation is 0.02023 p.u., lowest node voltage 0.9132p.u. parameter set: population size NP is 5, maximum number of generations of variation Gmax60, the scaling factor F is 0.5 and the crossover factor CR is 0.5.
TABLE 1 installation location, type and Capacity
Figure BDA0002693685260000071
While the present invention has been described in terms of its functions and operations with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise functions and operations described above, and that the above-described embodiments are illustrative rather than restrictive, and that various changes and modifications may be effected therein by one skilled in the art without departing from the scope or spirit of the invention as defined by the appended claims.

Claims (4)

1. A DG planning method based on an improved mixed integer differential evolution algorithm is characterized by comprising the following processes:
the method comprises the following steps: establishing a line loss minimum objective function of power distribution network reconstruction;
step two: establishing constraint conditions of power distribution network reconstruction, including power flow constraint, voltage constraint, DG power constraint and branch power constraint;
step three: and solving by adopting an improved mixed integer differential evolution algorithm to obtain the DG configuration of the power distribution network which meets the constraint condition and has the minimum line loss.
2. The DG planning method based on the improved mixed integer differential evolution algorithm of claim 1, wherein the line loss minimum objective function in the first step is:
Figure FDA0002693685250000011
in the formula: r isijIs the resistance of branch i-j; pij、QijRespectively the active power and the reactive power flowing through each time period at the tail ends of the branches i-j; vijIs the node voltage at the end of branch i-j; n is the number of nodes in the power distribution network; k is a radical ofijThe state variable of the branch i-j switch is 0-1 discrete quantity, wherein 0 represents opening, and 1 represents closing; minF is a minimum function of line loss.
3. The DG planning method based on the improved mixed integer differential evolution algorithm of claim 1, wherein the constraint conditions for the reconstruction of the distribution network in the second step are calculated according to the following formula:
and (3) power flow constraint:
Figure FDA0002693685250000012
Figure FDA0002693685250000013
in the formula: pi、QiRespectively input active power and reactive power of a node i; pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDi、QDiRespectively the active power and the reactive power of the load at the node i; u shapei、UjVoltages of nodes i and j, respectively; gij、BijAnd deltaijRespectively the conductance, susceptance and phase angle difference of the branches i-j; n is the number of nodes in the power distribution network;
voltage constraint:
Figure FDA0002693685250000021
in the formula: n is the number of nodes in the power distribution network; u shapei,minAnd Ui,maxRespectively representing the lower limit and the upper limit of the allowable voltage of the node i; i isijIs the current of branch i-j; i isij,maxThe upper limit of the current for branch i-j;
DG power constraint:
Figure FDA0002693685250000022
in the formula: pDGi、QDGiRespectively injecting active power and reactive power into the node i for the DG; pDG,max、PDG,minThe maximum value and the minimum value of the active power of the DG are respectively; qDG,max、QDG,minMaximum and minimum reactive power of DG respectively;
branch power constraint:
Figure FDA0002693685250000023
in the formula:
Figure FDA0002693685250000024
respectively the power and maximum power of the branches i-j.
4. The DG planning method based on the improved mixed integer differential evolution algorithm of claim 1, wherein the solving process adopting the improved mixed integer differential evolution algorithm in the third step is as follows:
step 1: initializing parameter population size NP (total number of individuals), wherein each individual is a D-dimensional vector, a scaling factor F and a cross factor CR;
step 2: randomly generating an initial population P in a decision space;
step 3: calculating the fitness of each individual, namely an objective function value;
step 4: randomly selecting three different individuals
Figure FDA0002693685250000025
(r1≠r2≠r3) G is an algebra;
step 5: variation of individuals in a population:
Figure FDA0002693685250000026
i＝1,2,...,NP，Vi Gis an individual after mutation;
step 6: crossing individuals in the population;
step 7: and (3) selecting the individuals in the population: when new vector individuals
Figure FDA0002693685250000027
Is more than the target vector individual
Figure FDA0002693685250000031
When better, reserve
Figure FDA0002693685250000032
To the next generation population, otherwise target vector individuals
Figure FDA0002693685250000033
Still remain in the population, once again as the next generation parent vector;
step 8: and if the maximum iteration number is reached or the error requirement is met, obtaining the optimal DG configuration result, and if not, returning to Step 3.
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