CN106169773B - Power supply optimization method for intelligent power distribution network comprising distributed power generation equipment - Google Patents

Power supply optimization method for intelligent power distribution network comprising distributed power generation equipment Download PDF

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CN106169773B
CN106169773B CN201610755691.5A CN201610755691A CN106169773B CN 106169773 B CN106169773 B CN 106169773B CN 201610755691 A CN201610755691 A CN 201610755691A CN 106169773 B CN106169773 B CN 106169773B
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王昕�
郑益慧
李立学
胡博
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Shanghai Jiaotong University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/388—Islanding, i.e. disconnection of local power supply from the network

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Abstract

The invention provides a power supply optimization method for an intelligent power distribution network containing distributed power generation equipment, which comprises the following steps of S1: weighting the load in the power distribution network according to the power loss load value of each industry; s2: providing a power distribution scheme solution space, and acquiring a solution from the power distribution scheme solution space as an initial power distribution scheme; s3: calculating each island range of the power distribution network under the power supply distribution scheme; s4: determining the optimal island range of the current power supply distribution scheme according to the total load value in each island range; s5: providing an optimization condition, jumping to the step S7 when the obtained power supply distribution scheme meets the optimization condition, otherwise jumping to the step S6; s6: calculating the power distribution scheme in the step S5 according to the firefly algorithm, generating a new power distribution scheme, and returning to the step S3; s7: and supplying power according to the current optimal power supply distribution scheme of the distributed power generation power supply and the final optimal island range. And the power supply of the distributed power generation equipment to important loads in a power distribution area after the system fails is ensured.

Description

Power supply optimization method for intelligent power distribution network comprising distributed power generation equipment
Technical Field
The invention relates to the technical field of electric power operation, in particular to a power supply optimization method for an intelligent power distribution network with distributed power generation equipment.
Background
In recent years, due to the increase of global energy demand and the destruction of energy by fossil fuels, new energy power generation technology has been developed sufficiently, and more new energy power generation equipment are connected to the system. These new energy generation devices are typically in the form of small-scale distributed power sources that are brought into proximity of the users of the distribution network. Besides meeting daily energy requirements, DG (distributed generation equipment) can also guarantee the power supply of important loads of a power distribution network when an external power grid fails, and IEEEStd.1547-2011 also changes the situation that the power grid is prevented from carrying out isolated island operation in the original IEEEStd.2000.929 into the situation that planned isolated island operation is realized by supporting the power grid and a user through a technical means.
At present, the research aiming at the problem of the island operation range of a power distribution network after DG access mainly aims at optimizing one or more of conditions such as improving the power supply range, reducing the power supply network loss, guaranteeing the power supply to important loads and the like under the constraint conditions such as power generation capacity, line transmission capacity, network connectivity and the like, and the optimal island operation range is calculated through optimization algorithms such as Kruskal algorithm, genetic algorithm and the like.
Disclosure of Invention
The invention aims to provide a power supply optimization method for an intelligent power distribution network containing distributed power generation equipment, which is used for quickly calculating a distribution scheme of the capacity of the distributed power generation equipment and a corresponding island range and ensuring that the distributed power generation equipment supplies power to important loads in a power distribution area after a system fault occurs.
In order to solve the problems, the invention provides a power supply optimization method for an intelligent power distribution network containing distributed power generation equipment, which comprises the following steps:
s1: weighting the load in the power distribution network according to the power loss load value of each industry;
s2: providing a power distribution scheme solution space, and acquiring a solution from the power distribution scheme solution space as an initial power distribution scheme;
s3: calculating each island range of the power distribution network under the power supply distribution scheme;
s4: determining the optimal island range of the current power supply distribution scheme according to the total load value in each island range;
s5: providing an optimization condition, jumping to the step S7 when the obtained power supply distribution scheme meets the optimization condition, otherwise jumping to the step S6;
s6: calculating the power distribution scheme in the step S5 according to the firefly algorithm, generating a new power distribution scheme, and returning to the step S3;
s7: and supplying power according to the current optimal power supply distribution scheme of the distributed power generation power supply and the final optimal island range.
According to an embodiment of the present invention, in step S1, the load in the distribution network is weighted according to the power loss load value of each industry, and the power loss is minimized with the goal of minimizing the power loss, wherein the weight of the load is increased as the power loss load value is increased, and the weight of the load is decreased as the power loss load value is decreased.
According to the inventionIn one embodiment, in the step S1, the power loss load value VjFrom direct power value VdjAnd indirect electric power value VijTwo parts are formed, and the calculation formula is
Vj＝Vdj+Vij(1)
Vdj＝Nj/Gkj(2)
Figure GDA0002441212860000021
Wherein N isjIncreased value for yield of j department, GkjIs the power consumption of j department, formula 3 is the indirect power value, which is the value of j department indirectly generated by unit power of power department, XijIs intermediate consumption, is the product quantity value of the j department required by the i department production, GkIs representative of the total power generation in the power sector, XjIs the total throughput for department j.
According to an embodiment of the present invention, in step S2, the power distribution solution space is a distribution solution of a group of distributed power generation devices accessing the power distribution network, and any solution is obtained from the distribution solution as an initial power distribution solution; the solution space satisfies that the dimensionality is the same as the number of access points, and each element solution is a nonzero-value vector.
According to an embodiment of the present invention, in step S4, a total load value of each islanding range is obtained according to a weight corresponding to each load and load in each islanding range, and an islanding range with the maximum total load value is used as an optimal islanding range of the current power distribution scheme.
According to an embodiment of the present invention, in the step S5, the optimization condition is an iteration number threshold or an optimization target value, when the iteration number of the power distribution scheme obtained through iterative computation according to the firefly algorithm reaches the iteration number threshold, or when the comparison value between the current power distribution scheme and the last power distribution scheme is smaller than the optimization target value, the step S7 is skipped, otherwise, the step S6 is skipped; wherein, when the power distribution scheme is obtained for the first time, the process directly jumps to the step S6.
According to one embodiment of the invention, calculating a power distribution scheme according to a firefly algorithm, generating a new set of power distribution schemes, comprises:
assuming that the power distribution scheme to be optimized is d-dimensional, a population of fireflies is first initialized in solution space
Figure GDA0002441212860000031
The initial light intensity of a certain firefly, namely the intensity of the emitted light at the position with zero distance from the firefly, is recorded as Ii，IiAnd
Figure GDA0002441212860000032
the objective function values at are equal, i.e.:
Figure GDA0002441212860000033
the intensity of the light emitted by firefly I transmitted at firefly j is IijWhich is the same as r2Proportional ratio, and satisfies the formula (7)
Figure GDA0002441212860000034
Wherein gamma is the light absorption coefficient, rijIs the Cartesian distance between fireflies and fireflies, i.e.
Figure GDA0002441212860000041
Assuming that the attraction of firefly i to firefly j is proportional to the relative brightness of firefly i at firefly j, the attraction β of firefly to firefly j can be obtained from the definition of the relative brightness of firefly iij(rij) Is composed of
Figure GDA0002441212860000042
The firefly j moves toward it to update its position due to the attraction of the firefly i, and the position of j is updated as follows (10):
Figure GDA0002441212860000043
where t is the number of iterations βij(rij) The attraction of firefly i to firefly j is calculated by equation 10, and α is the interval [0,1]The constant of (a) to (b) is,
Figure GDA0002441212860000044
is a random number vector obtained by gaussian distribution, uniform distribution or other distribution;
Figure GDA0002441212860000045
and (4) for a vector consisting of the capacities of the distributed power generation equipment of each access point, carrying out multiple iterations to find an optimal power supply distribution scheme meeting the optimization condition.
According to an embodiment of the present invention, in the steps S2 to S6, the solution is converged toward the optimal solution in an iterative process by using the firefly algorithm calculation.
According to one embodiment of the invention, the method is used for power supply target optimization under the condition that the power distribution network fails.
After the technical scheme is adopted, compared with the prior art, the invention has the following beneficial effects: aiming at an intelligent power distribution network containing a distributed power supply, load assignment is carried out in the power distribution network by utilizing a load loss value loss method, the power value of the load is evaluated, under the condition that an access point and the total capacity are fixed, the distributed power supply access capacity distribution is carried out by taking the minimum power failure loss as a target, the island range is calculated, the value of the power load in the island range is calculated, the advantages and the disadvantages of the island division range are evaluated, the distribution scheme of the capacity of distributed power generation equipment and the corresponding island range are rapidly calculated, the optimal power supply scheme is obtained, and the power supply of the distributed power generation equipment to important loads in a power distribution area after the system is in.
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Fig. 1 is a schematic flow chart of a power supply optimization method for a smart distribution network including distributed power generation equipment according to an embodiment of the present invention;
fig. 2 is a diagram of a model of lines 1 and 2 of a distribution network according to an embodiment of the present invention;
fig. 3 is a diagram of a model of lines 3 and 4 of the distribution network according to an embodiment of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather construed as limited to the embodiments set forth herein.
Referring to fig. 1, the method for optimizing power supply of the intelligent distribution network including the distributed power generation equipment according to the embodiment of the present invention includes the following steps:
s1: weighting the load in the power distribution network according to the power loss load value of each industry;
s2: providing a power distribution scheme solution space, and acquiring a solution from the power distribution scheme solution space as an initial power distribution scheme;
s3: calculating each island range of the power distribution network under the power supply distribution scheme;
s4: determining the optimal island range of the current power supply distribution scheme according to the total load value in each island range;
s5: providing an optimization condition, jumping to the step S7 when the obtained power supply distribution scheme meets the optimization condition, otherwise jumping to the step S6;
s6: calculating the power distribution scheme in the step S5 according to the firefly algorithm, generating a new power distribution scheme, and returning to the step S3;
s7: and supplying power according to the current optimal power supply distribution scheme of the distributed power generation power supply and the final optimal island range.
Preferably, the power supply optimization method for the intelligent power distribution network containing the distributed power generation equipment is used for power supply target optimization under the condition that the power distribution network fails.
In step S1, weights are assigned to the loads in the power distribution network according to the power loss load values of each industry, which may be weight assignment to the loads of each distribution box in the power distribution network in the fault occurrence area, so that the loads have different importance, and thus power can be supplied to important loads, and the value loss after power loss load is reduced.
The value of load loss (VOLL) refers to the loss of national economy and the like due to power shortage, and is a standard for measuring the loss of power shortage. In step S1, the load in the distribution network is weighted according to the power loss load value of each industry, the weight of the load is increased as the power loss load value is increased, and the weight of the load is decreased as the power loss load value is decreased, so as to minimize the power loss.
The power loss load values under different loss load levels can be obtained through the existing input-output method, so that the power loss is minimum or as small as possible, and the power loss is not expanded. The power value Vlj of electricity used by each department, i.e., the power value of electricity used by each department, is composed of a direct power value Vdj and an indirect power value Vij, which are caused by increasing the electricity consumption of each unit (generally 1kWh) by the department.
In one embodiment of the invention, in step S1, the power loss load value VjFrom direct power value VdjAnd indirect electric power value VijTwo parts are formed, and the calculation formula is
Vj＝Vdj+Vij(1)
Vdj＝Nj/Gkj(2)
Figure GDA0002441212860000061
Wherein N isjIncreased value for yield of j department, GkjIs the power consumption of j department, formula 3 is the indirect power value, which is the value of j department indirectly generated by unit power of power department, XijIs intermediate consumption, is the product quantity value of the j department required by the i department production, GkIs representative of the total power generation in the power sector, XjIs the total throughput for department j. The power load value of each production department is determined in this way, and the power value of each department obtained from equations (1) to (3) is shown in table 1, but not limited thereto.
Figure GDA0002441212860000071
TABLE 1 production load Power value and load weight
The weighting of the load of the power, heat and water production supply is set according to the table to be, for example, 1/Mw, and the weighting of the other departments is set in proportion to the power value thereof, and is, of course, only exemplary.
In step S2, a power distribution scheme solution space is provided, from which a solution is obtained as an initial power distribution scheme. The power supply distribution scheme solution space is a distribution scheme of a group of distributed power generation equipment accessed to a power distribution network, and one solution is obtained from the distribution scheme as an initial power supply distribution scheme; the solution space satisfies that the dimension is the same as the number of access points (access loads), each element solution is a nonzero-value vector, and each element solution represents the capacity of the access point, so that the sum of the solution space is the total capacity.
In step S3, each islanding range of the distribution grid under the power distribution scheme is calculated. The island range is an operation range which meets the constraints of power grid structure connectivity, power balance, thermal stability and the like and is independently powered by a distributed power generation device. The island range can be calculated according to documents such as von snow leveling with distributed power distribution network island division method based on minimum spanning tree and improved genetic algorithm, distributed power island division-thank exine based on Floyd _ warshall algorithm and the like, and details are not repeated here.
S4: and determining the optimal island range of the current power supply distribution scheme according to the total load value in each island range. In an embodiment of the present invention, a total load value of each islanding range is obtained according to a weight corresponding to each load and load in each islanding range, and an islanding range with the maximum total load value is used as an optimal islanding range of the current power distribution scheme. Other island range search algorithms based on floyd-warshall, minimum spanning tree and the like can also select the optimal island range, but have the defects that the search is easily stopped on the locally optimal result and the like, and the detailed description is omitted.
S5: and providing an optimization condition, jumping to the step S7 when the obtained power supply distribution scheme meets the optimization condition, and otherwise jumping to the step S6. The optimization condition can be set, and can be iteration times of the firefly algorithm or a convergence condition.
Preferably, in step S5, the optimization condition is an iteration threshold or an optimization target value, when the iteration of the power distribution scheme obtained by iterative calculation according to the firefly algorithm reaches the iteration threshold, or when the comparison value between the current power distribution scheme and the last power distribution scheme is smaller than the optimization target value, the step S7 is skipped, otherwise, the step S6 is skipped; wherein, when the power distribution scheme is obtained for the first time, the process directly jumps to the step S6.
In step S6, the power distribution scheme in step S5 is calculated based on the firefly algorithm, a new power distribution scheme is generated, and the process returns to step S3.
In one embodiment, calculating a power distribution scheme according to a firefly algorithm generates a new set of power distribution schemes, including:
assuming that the power distribution scheme to be optimized is d-dimensional, a population of fireflies is first initialized in solution space
Figure GDA0002441212860000081
The initial light intensity of a certain firefly, namely the intensity of the emitted light at the position with zero distance from the firefly, is recorded as Ii，IiAnd
Figure GDA0002441212860000091
the objective function values at are equal, i.e.:
Figure GDA0002441212860000092
the intensity of the light emitted by firefly I transmitted at firefly j is IijWhich is the same as r2Proportional ratio, and satisfies the formula (7)
Figure GDA0002441212860000093
Wherein gamma is the light absorption coefficient, rijIs the Cartesian distance between fireflies and fireflies, i.e.
Figure GDA0002441212860000094
Assuming that the attraction of firefly i to firefly j is proportional to the relative brightness of firefly i at firefly j, the attraction β of firefly to firefly j can be obtained from the definition of the relative brightness of firefly iij(rij) Is composed of
Figure GDA0002441212860000095
The firefly j moves toward it to update its position due to the attraction of the firefly i, and the position of j is updated as follows (10):
Figure GDA0002441212860000096
where t is the number of iterations βij(rij) The attraction of firefly i to firefly j is calculated by equation 10, and α is the interval [0,1]The constant of (a) to (b) is,
Figure GDA0002441212860000097
is a random number vector obtained by gaussian distribution, uniform distribution or other distribution;
Figure GDA0002441212860000098
and (4) for a vector consisting of the capacities of the distributed power generation equipment of each access point, carrying out multiple iterations to find an optimal power supply distribution scheme meeting the optimization condition.
In steps S2 to S6, the solution is calculated by the firefly algorithm, and the solution is converged to the optimal solution in the iterative process. The optimized target value is the convergence target value.
In step S7, power is supplied according to the current optimal power distribution scheme of the distributed generation power supply and the final optimal island range.
The method comprises the steps that the weight of a load is determined based on the power loss load value, the correlation between the determination of the weight and load characteristics is strong, so an actual calculation example is adopted, the calculation example is a power distribution network system connected to a transformer substation in the northeast region, the system comprises four lines, the total capacity is 40MVA, the connected capacity is 8MVA of the total capacity, and an actual access point is near a residential area in the middle of the lines.
Fig. 2 and 3 show distribution networks corresponding to four lines actually connected to a certain substation, the numbers on the loads are determined by the load size on the left side of the diagonal line, the unit is Mw, the weight of the load on the right side of the diagonal line is determined by the lost load value, the direct and indirect yield values of each industry are calculated as shown in table 1, the circle mark position is the distributed power generation equipment access position, the total capacity is set to be 20% of the capacity of the substation, and the total capacity is 8 Mw. First, 10 sets of random values are generated in the solution space, and each set of random values represents a capacity allocation scheme. Calculating all island ranges generated by each scheme, comparing and selecting the optimal island range, calculating the weight synthesis of the load in the range, substituting the weight of each scheme into a firefly algorithm calculation formula, as shown in formula 10, generating 10 groups of random values again, and judging again until the iteration times or convergence reaches the standard. The access capacities and weights of the different lines are referred to in table 2.
Line 1 Line 2 Line 3 Line 4
Access capacity 0.862 2.573 0.974 3.591
Weight sum 32.436 61.611 13.768 56.781
Table 2 access capacity and weight integration for different lines
The load weight determined by the loss load value method can objectively reflect the value of the load, and makes up for the defect that the load is divided too roughly according to the load grades. Aiming at the condition that a load access point is fixed in actual engineering, sequential iterative computation is carried out by utilizing a firefly optimization algorithm, and an optimal distributed generation capacity distribution scheme with the maximum load weight and the maximum optimization target in an island range when the total capacity of the accessed DGs is constant can be quickly obtained.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to limit the scope of the claims, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the invention.

Claims (8)

1. A power supply optimization method for an intelligent power distribution network containing distributed power generation equipment is characterized by comprising the following steps:
s1: weighting the load in the power distribution network according to the power loss load value of each industry;
s2: providing a power distribution scheme solution space, and acquiring a solution from the power distribution scheme solution space as an initial power distribution scheme;
s3: calculating each island range of the power distribution network under the power supply distribution scheme;
s4: determining the optimal island range of the current power supply distribution scheme according to the total load value in each island range;
s5: providing an optimization condition, jumping to the step S7 when the obtained power supply distribution scheme meets the optimization condition, otherwise jumping to the step S6;
s6: calculating the power distribution scheme in the step S5 according to the firefly algorithm, generating a new power distribution scheme, and returning to the step S3;
s7: and supplying power according to the current optimal power supply distribution scheme of the distributed power generation power supply and the final optimal island range.
2. The method for optimizing power supply to the intelligent power distribution network including the distributed power generation facilities according to claim 1, wherein in the step S1, the load in the power distribution network is weighted according to the power loss load value of each industry, and the power loss is minimized with the goal of minimizing the power loss, wherein the weight of the load is increased when the power loss load value is increased, and the weight of the load is decreased when the power loss load value is decreased.
3. The method for optimizing power supply to the intelligent power distribution network comprising the distributed power generation equipment according to claim 1, wherein in step S2, the power distribution scheme solution space is a set of distribution schemes of the distributed power generation equipment connected to the power distribution network, and any solution is obtained from the distribution schemes as an initial power distribution scheme; the solution space satisfies that the dimensionality is the same as the number of access points, and each element solution is a nonzero-value vector.
4. The method for optimizing power supply to the intelligent power distribution network including the distributed power generation equipment according to claim 1, wherein in step S4, the total load value in each islanding range is obtained according to the weight corresponding to each load and load in each islanding range, and the islanding range with the maximum total load value is used as the optimal islanding range of the current power distribution scheme.
5. The power supply optimization method for the intelligent power distribution network with the distributed power generation equipment according to claim 1, wherein in the step S5, the optimization condition is an iteration threshold or an optimization target value, when the iteration of the power distribution scheme obtained through iterative calculation according to the firefly algorithm reaches the iteration threshold, or when the comparison value between the current power distribution scheme and the last power distribution scheme is smaller than the optimization target value, the step S7 is skipped, otherwise, the step S6 is skipped; wherein, when the power distribution scheme is obtained for the first time, the process directly jumps to the step S6.
6. The method for optimizing power supply to the intelligent power distribution network comprising the distributed power generation equipment according to claim 1, wherein the step of calculating the power distribution scheme according to the firefly algorithm to generate a new set of power distribution schemes comprises the following steps:
assuming that the power distribution scheme to be optimized is d-dimensional, a population of fireflies is first initialized in solution space
Figure FDA0002441212850000021
Figure FDA0002441212850000022
The initial light intensity of a certain firefly, namely the intensity of the emitted light at the position with zero distance from the firefly, is recorded as Ii，IiAnd
Figure FDA0002441212850000023
the objective function values at are equal, i.e.:
Figure FDA0002441212850000024
the intensity of the light emitted by firefly I transmitted at firefly j is IijWhich is the same as r2Proportional ratio, and satisfies the formula (7)
Figure FDA0002441212850000025
Wherein gamma is the light absorption coefficient, rijIs the Cartesian distance between fireflies and fireflies, i.e.
Figure FDA0002441212850000026
Assuming that the attraction of firefly i to firefly j is proportional to the relative brightness of firefly i at firefly j, the attraction β of firefly i to firefly j can be derived from the definition of the relative brightness of firefly iij(rij) Is composed of
Figure FDA0002441212850000027
The firefly j moves toward it to update its position due to the attraction of the firefly i, and the position of j is updated as follows (10):
Figure FDA0002441212850000031
where t is the number of iterations βij(rij) The attraction of firefly i to firefly j is calculated by equation 10, and α is the interval [0,1]The constant of (a) to (b) is,
Figure FDA0002441212850000032
is a random number vector obtained by gaussian distribution, uniform distribution or other distribution;
Figure FDA0002441212850000033
and (4) for a vector consisting of the capacities of the distributed power generation equipment of each access point, carrying out multiple iterations to find an optimal power supply distribution scheme meeting the optimization condition.
7. The method for optimizing power supply to the intelligent power distribution network comprising distributed power generation equipment according to claim 1, wherein in the steps S2 to S6, the solution is converged to the optimal solution in an iterative process by using a firefly algorithm for calculation.
8. The method for optimizing power supply to the intelligent power distribution network comprising the distributed power generation equipment according to claim 1, wherein the method is used for optimizing power supply targets under the condition that the power distribution network fails.
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