CN110994665A - Distributed photovoltaic multi-point access low-voltage distribution network site selection method - Google Patents

Distributed photovoltaic multi-point access low-voltage distribution network site selection method Download PDF

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CN110994665A
CN110994665A CN201910976640.9A CN201910976640A CN110994665A CN 110994665 A CN110994665 A CN 110994665A CN 201910976640 A CN201910976640 A CN 201910976640A CN 110994665 A CN110994665 A CN 110994665A
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voltage
distribution network
photovoltaic
distributed photovoltaic
voltage distribution
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梁海深
迟福建
王哲
李桂鑫
孙阔
张鑫
王庆彪
周建伟
夏冬
张章
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
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State Grid Tianjin Electric Power Co Ltd
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Abstract

The invention relates to a distributed photovoltaic multi-point access low-voltage distribution network site selection method, which optimizes photovoltaic access positions and capacities by taking minimum comprehensive voltage deviation as an optimization target under the condition that network frame parameters of a low-voltage distribution network, user load distribution, the number of photovoltaic to be accessed and power are known. The method provided by the invention can effectively calculate the photovoltaic site selection distribution condition, has the advantages of simplicity and practicality, and can provide reference for planning and designing the distributed photovoltaic access low-voltage distribution network.

Description

Distributed photovoltaic multi-point access low-voltage distribution network site selection method
Technical Field
The invention belongs to the field of distributed photovoltaic, and relates to a distributed photovoltaic high-density multi-point access low-voltage distribution network technology, in particular to a distributed photovoltaic multi-point access low-voltage distribution network site selection method.
Background
In recent years, the number of distributed photovoltaic high-density multipoint access rural low-voltage power distribution networks is increasing, and the low-voltage power distribution network equipment and line standards in rural areas are low, so that the problems of low voltage, three-phase imbalance and the like are prominent. The disordered access of the distributed photovoltaic further increases the influence on the production and life of the low-voltage distribution network and residential users, and the power quality, the network loss, the power distribution and the like are greatly changed along with the influence. Therefore, the problem of site selection and volume fixing of the photovoltaic access power distribution network is always the key point of research.
Various methods for solving the problems of location and volume of the distributed power supply are proposed at home and abroad, such as an opportunity constraint location and volume planning method for considering the intermittent output of the distributed power supply, a multi-target distributed power supply location and volume planning method for considering the time sequence characteristics, a distributed power supply optimization location problem in a large-scale interconnected power distribution network and the like, but research is carried out on a high-voltage power distribution network, and no discussion is carried out on the location problem of accessing a low-voltage power distribution network. In addition, there are also related documents which research the limit access capacity of distributed photovoltaic based on the constraint conditions such as voltage under the conditions of distributed power output and load distribution, but do not analyze the photovoltaic arrangement.
In summary, distributed photovoltaic site selection and volume fixing research in the existing literature mainly aims at high and medium voltage distribution networks, most of the distributed photovoltaic site selection and volume fixing research mainly takes minimum network loss or optimal economy as an objective function, constraint conditions such as voltage and power flow are considered for optimal configuration, complex power flow calculation is generally involved, the calculated amount is large, the difficulty is high, the engineering calculation practicability is lacked, and few researches are made on the site selection problem of the distributed photovoltaic high-density multi-point access low-voltage distribution network.
Disclosure of Invention
The invention aims at the problems of complex calculation, large quantity, high difficulty and lack of engineering calculation practicability under the premise of known planned photovoltaic scale and distributed photovoltaic access low-voltage distribution network influence analysis, establishes a distributed photovoltaic multi-point access low-voltage distribution network optimized site selection model by taking the minimum integrated voltage deviation of the distributed photovoltaic access low-voltage distribution network as a target, and provides a legacy algorithm-based optimization model solving method and a legacy algorithm-based optimization model solving process, so that site selection and distribution conditions of a planned distributed photovoltaic project can be obtained.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a distributed photovoltaic multi-point access low-voltage distribution network site selection method optimizes photovoltaic access positions and capacity by taking minimum comprehensive voltage deviation as an optimization target under the conditions of known low-voltage distribution network frame parameters, user load distribution, photovoltaic number to be accessed and power, wherein the optimization target function is as follows:
min f＝Uindex
in the formula: u shapeindexRepresenting the integrated voltage deviation; the calculation formula is shown as the formula,
Figure BDA0002233853380000021
in the formula: u shapeiRepresenting a node voltage magnitude; u shape0Represents a reference voltage value; n is a radical ofsRepresenting the number of 380V three-phase user nodes; n is a radical ofdRepresenting the number of 220V single-phase user nodes.
And, UiThe constraint conditions of (1) are:
Umin≤Ui≤Umax
in the formula: u shapeminIs the minimum allowed voltage amplitude; u shapemaxIs the maximum allowed voltage amplitude.
And, Ns、NdThe constraint conditions of (1) are:
Figure BDA0002233853380000022
in the formula: n is a radical ofDGs、NDGdThe number of three-phase and single-phase access distributed power supplies is respectively.
Furthermore, the limit transmission capacity of the line is set as a constraint condition of the objective function,
PDG.i-Pi≤SL
PDG.iis a distributed photovoltaic capacity; piIs the user load; sLThe limit transmission capacity of the distribution line.
And solving the optimized objective function by adopting a genetic algorithm. The solving process is as follows:
1) arranging node numbers capable of installing distributed photovoltaics into arrays, using 0 or 1 to represent whether the nodes are installed or not, using 0 to represent that the photovoltaics are not installed, using 1 to represent that the photovoltaics are installed, so that each individual is an array only containing 0 or 1, the sum of array elements is the number of users capable of installing the photovoltaics, and generating an initial solution by a random method;
2) obtaining the fitness value of each individual by calculating voltage deviation, then carrying out copy operation according to the fitness of each individual, then carrying out crossover and mutation operation to generate next generation individuals, wherein the number of 0 and 1 in the generated individuals can be changed, so that the constraint condition of the number of distributed photovoltaic installations can not be met, and therefore the individuals which do not meet the condition need to be processed;
3) and when the maximum algebra C of genetic operation is met or the continuous T generations of the optimal solution are not changed, the genetic optimization is ended, and the optimal individuals in the population and the fitness value thereof are the final result.
The invention has the advantages and positive effects that:
the distributed photovoltaic high-density multipoint access low-voltage distribution network can cause obvious voltage deviation and has important influence on safety of household appliances and electricity consumption of users, so that the invention provides a distributed photovoltaic optimal site selection method for the low-voltage distribution network, which takes minimum comprehensive voltage deviation as a target function, aiming at the problem of site selection of the distributed photovoltaic access low-voltage distribution network. Through analysis, the method provided by the invention can effectively calculate the photovoltaic site selection distribution condition, has the advantages of simplicity and practicality, and also shows that in a low-voltage distribution network in a platform area, the photovoltaic tends to be distributed to the tail end of a line. The method provided by the invention can also provide reference for planning and designing the distributed photovoltaic access low-voltage distribution network.
Drawings
FIG. 1 is a flow diagram of a non-feasible solution processing method;
FIG. 2 illustrates a low voltage distribution network and distribution of users;
fig. 3 shows the photovoltaic addressing results.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments, which are illustrative only and not limiting, and the scope of the present invention is not limited thereby.
A distributed photovoltaic multi-point access low-voltage distribution network site selection method optimizes photovoltaic access positions and capacity by taking minimum comprehensive voltage deviation as an optimization target under the condition that network frame parameters, user load distribution, the number of photovoltaic to be accessed and power of a low-voltage distribution network are known.
Firstly, establishing an objective function:
the method takes the minimum comprehensive voltage deviation as an optimization target, takes a user load value as weight, and respectively calculates the sum of single-phase voltage deviation and three-phase voltage deviation for representing the overall voltage distribution condition of the low-voltage distribution network. As shown in formula (1).
min f＝Uindex(1)
In the formula: u shapeindexRepresenting the integrated voltage deviation; the calculation formula is shown as formula (2).
Figure BDA0002233853380000031
In the formula: u shapeiRepresenting a node voltage magnitude; u shape0Represents a reference voltage value; n is a radical ofsRepresents the number of 380V three-phase user nodes；NdRepresenting the number of 220V single-phase user nodes.
Secondly, setting constraint conditions:
considering that the problem of electric energy quality cannot be caused when distributed photovoltaic power generation is connected to a low-voltage distribution network, and simultaneously considering the problem of three-phase imbalance caused by mixed connection of single-phase load and three-phase load, the constraint conditions of the model are as follows:
1) node voltage constraint
Umin≤Ui≤Umax(3)
The formula shows that in a low-voltage distribution network, the voltage of any one user node cannot exceed the limit. In the formula: u shapeminIs the minimum allowable voltage amplitude; u shapemaxIs the maximum allowed voltage amplitude.
2) Line limit delivery capacity constraints
PDG.i-Pi≤SL(4)
The formula indicates that any distributed photovoltaic reverse power cannot exceed the limit transmission capacity of the distribution line. In the formula: pDG.iIs a distributed photovoltaic capacity; piIs the user load; sLThe limit transmission capacity of the distribution line.
3) Accessible node number constraints
Figure BDA0002233853380000041
The formula represents the number of three-phase and single-phase distributed photovoltaic power generation which can be accessed in the low-voltage distribution network. In the formula: n is a radical ofDGs、NDGdThe number of three-phase and single-phase access distributed power supplies is respectively; n is a radical ofs、NdRespectively representing the maximum allowable access quantity of three phases and single phase.
Thirdly, establishing a solving method
Firstly, approximate processing of constraint conditions is carried out
The voltage deviation can be calculated approximately by the line voltage loss, and the line voltage drop calculation formula is shown in formula (6).
Figure BDA0002233853380000042
In the formula: p represents the active power passing on the line; q represents the reactive power passing on the line; r represents a line resistance; x represents the line reactance; u shapeNRepresenting the nominal voltage of the line.
According to the regulation of GB/T12325 power quality supply voltage deviation, 380V three-phase supply voltage deviation is +/-7% of the nominal voltage, and 220V single-phase supply voltage deviation is + 7% and-10% of the nominal voltage. Therefore, the photovoltaic is accessed through 380V and 220V, and the upper limit of the voltage deviation is 7 percent.
And then, solving the flow, namely solving the optimization model by adopting a genetic algorithm. And taking the optimization target as the fitness function of the individual, wherein the smaller the fitness is, the better the individual is. The specific process is shown in figure 1.
The solving process is as follows:
1) chromosomal coding
The node numbers capable of installing the distributed photovoltaic are arranged into arrays, whether the distributed photovoltaic is installed or not is indicated by 0 or 1, the photovoltaic is not installed by 0, the photovoltaic is installed by 1, therefore, each individual is an array only containing 0 or 1, and the sum of the array elements is the number of users capable of installing the photovoltaic. The initial solution is generated by a random method.
2) Inheritance, cross and variation
The fitness value of each individual is obtained by calculating the voltage deviation, then the copying operation is carried out according to the fitness of each individual, then the crossing and mutation operation is carried out, and the next generation of individuals are generated, at the moment, the number of 0 and 1 in the generated individuals may change, so that the constraint condition of the number of the distributed photovoltaic installations may not be met, and therefore the individuals which do not meet the condition need to be processed. The method steps of the treatment are shown in fig. 1.
3) Optimizing termination
When the maximum generation C of genetic operation is met or the continuous T generation of the optimal solution does not change, the genetic optimization is terminated, and the optimal individuals in the population and the fitness value thereof are the final result.
Selecting a distributed photovoltaic poverty-relieving project of a certain village as a case, wherein 25 residents are shared in the village and are poverty-relieving customers, the photovoltaic 4kWp multiplied by 8 and 10kWp multiplied by 3 are planned to be installed in the village according to the plan, the distributed photovoltaic projects are 11 in total, the capacity is 62kWp, all the customers in the village have photovoltaic installation conditions, and the project has good lighting conditions. The user distribution is shown in fig. 2.
The distribution transformer rated capacity of the low-voltage distribution network in the village is 100kVA, the low-voltage distribution lines all adopt LGJ-35, the rated current-carrying capacity is 170A, the resistance per unit length is 0.85 omega/km, and the reactance per unit length is 0.417 omega/km. The user load at the time of maximum photovoltaic output is taken, and the data of each user load is shown in table 1.
TABLE 1 user load data
Figure BDA0002233853380000051
The population size is set to be 30, the replication probability is 0.2, the cross probability is 0.6, the mutation probability is 0.002, the maximum generation C of genetic operation is set to be 200, and the maximum generation T of optimal solution without change is set to be 10. Aiming at the above calculation example, the optimization model is used for calculation, and the red square frame shown in fig. 3 is used for installing photovoltaic users and capacity to obtain the optimization result of the village distributed photovoltaic site selection.
As can be seen from fig. 3, under the condition that the comprehensive voltage deviation of the distribution area is minimum, the voltage deviation and the power constraint condition are satisfied, the access distribution condition of 11 proposed distributed photovoltaic projects can be obtained, and according to the distribution result, the distributed photovoltaic site selection optimization position is biased to the middle terminal of the low-voltage distribution line, the main reason is that the voltage at the tail terminal of the low-voltage distribution line is normally low, and the node voltage is raised after the photovoltaic is accessed, so that under the optimal constraint condition, the distributed photovoltaic is concentrated to the middle terminal. However, it should be noted that for 10kWp distributed photovoltaic, three-phase access is required, and for single-phase users, a network connection line and a related metering device are required to be added, so that investment is increased, and therefore, the 10kWp photovoltaic can be accessed to the three-phase users as much as possible.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various changes and modifications can be made without departing from the inventive concept, and these changes and modifications are all within the scope of the present invention.

Claims (6)

1. A distributed photovoltaic multi-point access low-voltage distribution network site selection method is characterized by comprising the following steps: under the condition that the grid frame parameters of the low-voltage distribution network, the user load distribution, the number of photovoltaic cells to be accessed and the power are known, the photovoltaic access position and the photovoltaic capacity are optimized by taking the minimum comprehensive voltage deviation as an optimization target, and the optimization target function is as follows:
min f＝Uindex
in the formula: u shapeindexRepresenting the integrated voltage deviation; the calculation formula is shown as the formula,
Figure FDA0002233853370000011
in the formula: u shapeiRepresenting a node voltage magnitude; u shape0Represents a reference voltage value; n is a radical ofsRepresenting the number of 380V three-phase user nodes; n is a radical ofdRepresenting the number of 220V single-phase user nodes.
2. The method for locating a distributed photovoltaic multipoint access low-voltage distribution network according to claim 1, wherein: u shapeiThe constraint conditions of (1) are:
Umin≤Ui≤Umax
in the formula: u shapeminIs the minimum allowed voltage amplitude; u shapemaxIs the maximum allowed voltage amplitude.
3. The method for locating a distributed photovoltaic multipoint access low-voltage distribution network according to claim 1, wherein: n is a radical ofs、NdThe constraint conditions of (1) are:
Figure FDA0002233853370000012
in the formula: n is a radical ofDGs、NDGdDistributed electricity for three-phase and single-phase access respectivelyThe number of sources.
4. The method for locating a distributed photovoltaic multipoint access low-voltage distribution network according to claim 1, wherein: the line limit delivery capacity is set as a constraint of an objective function,
PDG.i-Pi≤SL
PDG.iis a distributed photovoltaic capacity; piIs the user load; sLThe limit transmission capacity of the distribution line.
5. The method for locating a distributed photovoltaic multipoint access low-voltage distribution network according to claim 1, wherein: and solving the optimized objective function by adopting a genetic algorithm.
6. The method for locating a distributed photovoltaic multipoint access low-voltage distribution network according to claim 5, wherein: the solving process is as follows:
1) arranging node numbers capable of installing distributed photovoltaics into arrays, using 0 or 1 to represent whether the nodes are installed or not, using 0 to represent that the photovoltaics are not installed, using 1 to represent that the photovoltaics are installed, so that each individual is an array only containing 0 or 1, the sum of array elements is the number of users capable of installing the photovoltaics, and generating an initial solution by a random method;
2) obtaining the fitness value of each individual by calculating voltage deviation, then carrying out copying operation according to the fitness of each individual, then carrying out crossover and mutation operation to generate next generation individuals, wherein the number of 0 and 1 in the generated individuals can be changed, so that the constraint condition of the number of distributed photovoltaic installations can not be met, and therefore the individuals which do not meet the condition need to be processed;
3) when the maximum generation C of genetic operation is met or the continuous T generation of the optimal solution does not change, the genetic optimization is terminated, and the optimal individuals in the population and the fitness value thereof are the final result.
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