CN111091307A - Power distribution network resource processing method, storage medium and processor - Google Patents
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Abstract
The invention discloses a power distribution network resource processing method, a storage medium and a processor. Wherein, the method comprises the following steps: acquiring a pre-increased load of the power distribution network; establishing a target model, wherein the target model is used for evaluating the adjustment of the resources of the power distribution network under the condition that the power distribution network meets the load requirement; inputting at least one constraint to the target model, wherein the constraint is indicative of a limited range of a predetermined parameter of the distribution network if the load requirement is met; and obtaining a mode for adjusting the resources of the power distribution network according to the target model. The invention solves the technical problem of unreasonable resource optimization configuration of the power distribution network in the prior art.
Description
Technical Field
The invention relates to the technical field of power distribution network resource processing, in particular to a power distribution network resource processing method, a storage medium and a processor.
Background
The power consumption characteristics of the electric heating equipment are influenced by various factors such as equipment types, power consumption time, geographical positions and the like, the characteristics of high power, randomness, intermittence, dispersity, centralized concurrency and the like are shown, along with the deep process of changing coal into electricity, a series of problems are brought to a power grid by large-scale distributed electric heating load access, and the heating effect of a user changing coal into electricity and the transformation, planning and construction effects of a power distribution network are directly influenced.
A mathematical model used for researching the problem of the optimal configuration of the power distribution network resources abroad is mainly a power distribution network extended construction resource optimal configuration model. Related researches on optimal configuration and operation of power distribution network resources in China comprise research results in theoretical aspects and mature planning technical standards. However, the characteristics of the electric heating load and the planning and operation problems of the power distribution network are not considered in the current researches.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a power distribution network resource processing method, a storage medium and a processor, which are used for at least solving the technical problem that the optimal configuration of power distribution network resources is unreasonable in the prior art.
According to an aspect of the embodiments of the present invention, a method for processing power distribution network resources is provided, including: acquiring a pre-increased load of the power distribution network; establishing a target model, wherein the target model is used for evaluating the adjustment needed to be carried out on the resources of the power distribution network under the condition that the power distribution network meets the load requirement; inputting at least one constraint to the target model, wherein the constraint is indicative of a limited range of a predetermined parameter of the distribution grid if the load requirement is met; and obtaining a mode for adjusting the resources of the power distribution network according to the target model.
Optionally, the target model comprises two layers, wherein a first layer model of the two layers is used for adjusting the installation location and/or capacity of the resource in the power distribution network; the second of the two layers is used to adjust the configuration of resources in the power distribution network.
Optionally, the method further comprises: the second layer model feeds back the adjustment result of the configuration of the resource to the first layer model as one of the bases for the first layer model to evaluate the installation position and/or capacity adjustment of the resource.
Optionally, the method further comprises: and the first layer model feeds back the adjustment result of the resource installation position and/or capacity of the first layer model to the second layer model to serve as a constraint condition for the second layer model to adjust the configuration of the resource.
Optionally, the first layer model uses the following objective function: fupper＝min(ω1f1+ω2f2+ω3f3) (ii) a Wherein f is1Annual loss charge of distribution line, f2For the efficiency of utilization of the distribution network equipment, f3For operating maintenance costs, omega, of distribution network equipment1、ω2、ω3Are respectively f1、f2、f3The weight occupied.
Optionally, the constraints of the first layer model include at least one of: node voltage, power balance, reliability, network topology, investment of distribution lines, wire cross sections, power supply radius, switch allowable action times, investment of switch equipment, investment of reactive compensation devices and investment of three-phase unbalance adjusting devices.
Optionally, the first layer model uses the following objective function: flower＝min(ω3f3+ω4f4) (ii) a Wherein f is3For annual power loss of the distribution line, f4Is the annual voltage deviation minimum, omega3、ω4Are respectively f3、f4The weight occupied.
Optionally, the constraints of the second layer model include at least one of: node voltage, power balance, branch power, network topology, the number of allowed actions of the switch, and the number of three-phase imbalance adjusting devices.
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and when the program runs, a device in which the storage medium is located is controlled to execute any one of the above methods.
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes to perform the method of any one of the above.
In the embodiment of the invention, the method comprises the steps of acquiring the pre-increased load of the power distribution network; establishing a target model, wherein the target model is used for evaluating the adjustment needed to be carried out on the resources of the power distribution network under the condition that the power distribution network meets the load requirement; inputting at least one constraint to the target model, wherein the constraint is indicative of a limited range of a predetermined parameter of the distribution grid if the load requirement is met; the method for adjusting the resources of the power distribution network is obtained according to the target model, and the resources of the power distribution network are adjusted through the target model, so that the purposes of lowest power distribution loss, least investment and highest utilization efficiency of power grid equipment, namely the optimal comprehensive target, are achieved, the technical effect of effectively reducing power grid investment and power distribution loss is achieved, and the technical problem of unreasonable resource optimization configuration of the power distribution network in the prior art is solved.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of a power distribution network resource processing method according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
In accordance with an embodiment of the present invention, there is provided an embodiment of a method for processing power distribution grid resources, it should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than that described herein.
Fig. 1 is a flowchart of a power distribution network resource processing method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, acquiring a pre-increased load of the power distribution network;
the power distribution network includes but is not limited to a 10kV line, and the pre-increased load of the power distribution network can be from electric heating equipment, and of course, can also be from other electric equipment, such as an air conditioner and the like.
Step S104, establishing a target model, wherein the target model is used for evaluating the adjustment of the resources of the power distribution network under the condition that the power distribution network meets the load requirement;
the target model can improve the voltage quality of the power distribution network, guarantee the power supply reliability and power supply capacity of the power distribution network, and realize economic and safe operation of the power distribution network.
Step S106, inputting at least one constraint condition to the target model, wherein the constraint condition is used for indicating the limited range of the preset parameter of the power distribution network under the condition of meeting the load requirement;
the constraint conditions relate to topological constraint, relevant regulations, energy balance and guarantee of voltage quality, power supply reliability and power supply capacity of the power grid.
And S108, obtaining a mode for adjusting the resources of the power distribution network according to the target model.
In the specific implementation process, the power distribution network resources such as the wiring mode, the line model, the line power supply radius, the distribution transformer model, the distribution transformer capacity, the reactive power compensation device, the three-phase imbalance adjusting device and the like can be adjusted to be reasonably and optimally utilized. Optionally, the adjusting manner includes at least one of the following: and additionally installing a three-phase unbalance adjusting device, configuring a distribution transformer reactive compensation device and a capacity regulating transformer.
Through the steps, the method can realize the adoption of the method for acquiring the pre-increased load of the power distribution network; establishing a target model, wherein the target model is used for evaluating the adjustment of the resources of the power distribution network under the condition that the power distribution network meets the load requirement; inputting at least one constraint to the target model, wherein the constraint is indicative of a limited range of a predetermined parameter of the distribution network if the load requirement is met; the method for adjusting the resources of the power distribution network is obtained according to the target model, and the resources of the power distribution network are adjusted through the target model, so that the purpose of optimal comprehensive target with lowest power distribution loss, lowest investment and highest utilization efficiency of power grid equipment is achieved, the technical effect of effectively reducing power grid investment and power distribution loss is achieved, and the technical problem of unreasonable resource optimization configuration of the power distribution network in the prior art is solved.
Optionally, the target model comprises two layers, wherein a first layer model of the two layers is used for adjusting the installation location and/or capacity of the resource in the power distribution network; the second of the two layers is used to adjust the configuration of resources in the power distribution network.
The first layer model may be an upper layer model, and the second layer model may be a lower layer model.
For example, a two-layer planning method can be adopted to perform integrated planning on the power distribution network resources, and a general model of the two-layer planning is as follows:
wherein, F (x, w) and F (x, y) are respectively an upper layer objective function and a lower layer objective function; g (x) and g (x, y) are respectively upper and lower layer constraint conditions; x and y are decision variables of an upper layer and a lower layer respectively.
The upper layer is an investment decision problem, and a multi-target model is established by considering the investment cost of equipment such as a distribution line, a distribution transformer, a reactive power compensation device, a switch device, a three-phase imbalance adjusting device and the like and the economic and safe promotion effect of the equipment on the distribution network. The method is characterized in that the installation positions and the capacities of various resources of the power distribution network are optimized by taking the minimum investment cost and annual network loss cost of various power distribution network resources and the highest utilization efficiency of power grid equipment as optimization targets.
The lower layer is an operation optimization problem, the network reconstruction is considered, reactive compensation equipment and a three-phase imbalance adjusting device are additionally arranged, the effects of reducing the network loss and improving the voltage quality are considered, and a multi-objective optimization model is also established. And optimizing the configuration conditions of various power distribution network resources by taking the minimum network loss and voltage deviation after the heating load is accessed as an optimization target.
As an optional embodiment, according to seasonal characteristics of the electric heating load, the whole year can be divided into 3 typical energy supply seasons, namely a heating season (11 months to 3 months in the next year), a cooling season (6 months to 9 months) and a transition season (4 months, 5 months and 10 months), the whole year is divided into 3 typical seasons, 1 representative day is selected for each season, and the operation fee of the system in each typical day can be calculated firstly and then multiplied by corresponding days to obtain the annual operation fee.
Optionally, the method further comprises: the second layer model feeds back the adjustment result of the configuration of the resource to the first layer model as one of the bases for the first layer model to evaluate the installation position and/or capacity adjustment of the resource.
Optionally, the method further comprises: and the first layer model feeds back the adjustment result of the resource installation position and/or capacity to the second layer model as a constraint condition for adjusting the configuration of the resource by the second layer model.
The upper layer investment decision result acts on the lower layer objective function and the constraint condition, and the lower layer operation optimization is fed back to the upper layer by an optimal value, so that the interaction between the upper layer and the lower layer is realized. The planning model constraint relates to topology constraint, relevant regulations, energy balance and guarantee of voltage quality, power supply reliability and power supply capacity of the power grid.
Optionally, the first layer model uses the following objective function: fupper＝min(ω1f1+ω2f2+ω3f3) (ii) a Wherein f is1Annual loss charge of distribution line, f2For the efficiency of utilization of the distribution network equipment, f3For distribution network equipmentOperating maintenance costs of omega1、ω2、ω3Are respectively f1、f2、f3The weight occupied.
The annual loss of the distribution line is used
Wherein, taTotal days for season a; KS is the total time period obtained after the equivalent load curve is segmented; t is tjIs the duration of the jth period; plossjThe line loss electric quantity is the j time period; ceIs the electricity price.
The annual power loss cost
Wherein N isaThe total number of the power distribution network resources is; mu.siIndicating the utilization of the ith grid resource.
The operation and maintenance cost of the distribution network resources
Wherein, CIiInvestment cost for the ith grid resource unit; CO 2iThe operation and maintenance cost of the ith power grid resource unit.
After the large-scale electric heating equipment is connected to the power distribution network, the objective function comprehensively considers the line model, the wiring mode, the power supply radius, the positions of the contact and section switches, the configuration of the reactive compensation equipment and the configuration of the three-phase unbalance adjusting device to improve the line loss and the reliability, so that the investment and maintenance cost of the power distribution network resources and the annual network loss cost are reduced, and the utilization efficiency of the power distribution network equipment is improved to serve as an optimization target. In a specific implementation process, the resources can be optimized by the objective function, and the resources can be calculated in seasons and time intervals and then superposed.
Optionally, the constraints of the first layer model comprise at least one of: node voltage, power balance, reliability, network topology, investment of distribution lines, wire cross sections, power supply radius, switch allowable action times, investment of switch equipment, investment of reactive compensation devices and investment of three-phase unbalance adjusting devices.
The above node voltage constraints are: u shapemin≤Uk≤Umax；
The power balance constraint is:
wherein, Pi、QiRespectively injecting active power and reactive power into the node i; gij、Bij、δijSequentially setting the conductance, susceptance and voltage phase angle difference between the nodes i and j; u shapei、UjThe voltage amplitudes of nodes i, j, respectively.
The above reliability constraints are: r is not less than R0Wherein R is0Is a predetermined achieved reliability index. The power supply reliability requirements are different for different power supply regions, the grid reliability requirement is higher than 99.83% for urban areas, and the grid reliability requirement is higher than 99.72% for rural areas.
The network topology constraint is that a network structure needs to meet a radiation structure and a connectivity structure, and each load point needs to be connected with a power grid, namely, a power supply supplies power; in the case of normal power supply, each load point can be supplied by only one power supply.
Above-mentioned distribution lines's investment constraint, wherein, distribution lines is 10kV distribution lines, and 10kV distribution lines's construction transformation principle is investment and working costs minimum principle, satisfies certain load requirement and safe and reliable constraint condition under, confirms the circuit construction scheme, and the constraint function is:
wherein, FNL1Annual investment and total running cost for newly building a 10kV medium-voltage line; fNL2Annual investment for expanding capacity of existing 10kV medium-voltage lineAnd total operating costs; fNKAnnual investment and total running cost of equipment such as switches, β is the price of electricity, i is the load point in the system, j is the possible power failure accident, npIs the set of all load points in the system; n iseThe set of all power outage events that may occur; l isiIs the load point λjThe load value of (d); the occurrence probability of the power failure time j; r isjThe duration of the power outage.
Annual investment and operating costs for the plant FNL1、FNL2And FNKThe comprehensive investment can be converted into the investment cost which is evenly distributed in each year by adopting a current value-to-year value method, and the specific calculation formula is as follows:
wherein, FNi、ZiAnd YiRespectively the annual comprehensive cost, the comprehensive investment and the annual operation cost (yuan/year) of each device; n isiThe economic service life of the equipment; r is0The investment recovery rate of the power industry is high.
The annual operating cost Yi mainly includes the annual energy loss cost of the equipment and the maintenance cost, and the like, as shown in the following formula:
Yi＝βΔAi+Wi
wherein, WiAnnual overhaul and maintenance cost of the equipment, β power generation ratio, delta AiThe total annual electric energy loss value of the equipment; the annual energy loss cost of the switch is negligible, i.e. Δ aK＝0。
The above-mentioned constraints on the section of the conductor, for example, the section of the trunk of a 10kV cable line in the "coal to electricity" region, is 300mm2The cross section of the branch line is 185mm2(ii) a The section of a 10kV overhead line trunk line is 240mm2The cross section of the branch line is 70mm2。
The above power supply radius constraint, for example, the power supply radius of the 10kV line in the "coal to electricity" area in the town is preferably controlled within 5km, and the power supply radius of the 10kV line in the "coal to electricity" area in the rural area is preferably controlled within 15 km.
Generally, the operation department has a corresponding limit to the number of switch operations in a day according to the "operation guide rule" rule, and the allowable number of switch actions is constrained by the following formula:
Nstotal≤Nstmax
Nsk≤Nskmax,k＝1,2,...,ns
wherein N isstotalRefers to the total operation times of the tie switch and the section switch in a fixed time period, and the upper limit is Nstmax；NskThe number of times of actions of the kth switch in the action switch set in one day is N, and the corresponding upper limit value is Nstmax，nsThe number of switches participating in the action.
The investment constraints of switchgear, such as the cost of line construction, are usually limited, and the investment constraints of the interconnection switches are introduced to limit the number of interconnection switches installed. Specifically, the following formula:
N·Cs≤Cinv
wherein N is the number of installation units of the interconnection switch; csThe current value of single investment for the interconnection switch; cinvIs the investment limit.
The investment constraints of the reactive power compensation device and the three-phase imbalance adjusting device, for example, the investment costs of the reactive power compensation device and the three-phase imbalance adjusting device are limited, the number of equipment required to be equipped is reduced as much as possible under the condition of ensuring the voltage quality, and the investment constraints of the reactive power compensation device and the three-phase imbalance adjusting device are introduced to limit the number of the equipment to be installed. Specifically, the following formula:
A·RS≤Rinv
B·SPCS≤SPCinv
wherein A is the number of groups of reactive compensation devices; b is the number of the three-phase unbalance adjusting devices; rsFor the present value of investment, R, of a single group of reactive power compensation devicesinvIs the investment limit of the reactive compensation device; SPCSThe investment current value of a single three-phase unbalance adjusting device is obtained; SPCinvIs a single three-phaseBalancing the investment limits of the regulating device.
Optionally, the first layer model uses the following objective function: flower＝min(ω3f3+ω4f4) (ii) a Wherein f is3For annual power loss of the distribution line, f4Is the annual voltage deviation minimum, omega3、ω4Are respectively f3、f4The weight occupied.
The annual power loss and power consumption of the distribution line:
wherein the content of the first and second substances,
for the electricity price in the time period t, the value of the electricity price is combined with the peak-valley electricity price policies of users in different heating areas, and the average value of the peak-valley electricity price is taken, for example, the average value of the electricity prices in Beijing is selected according to 0.26 yuan/kilowatt hour (the subsidy policy of the electricity price in Beijing is comprehensively considered);
is the power loss over time period t; Δ t is the time interval of each time interval; t is the number of time segments.
The voltage deviation described above:
wherein the content of the first and second substances,
the voltage of node k for a period t; vNIs the node rated voltage; n is a radical ofrIs a collection of nodes.
Optionally, the constraints of the second layer model include at least one of: node voltage, power balance, branch power, network topology, the number of allowed actions of the switch, and the number of three-phase imbalance adjusting devices.
The node voltage constraint is as follows:
Vmin≤Vk≤Vmax
wherein, Vmin、VmaxRespectively an upper limit and a lower limit of the node voltage; vkIs the voltage at node k.
The above power balance constraint:
wherein, Pi、QiRespectively injecting active power and reactive power into the node i; gij、Bij、δijSequentially setting the conductance, susceptance and voltage phase angle difference between the nodes i and j; n is the total number of system nodes; vi、VjThe voltage amplitudes of nodes i, j, respectively.
The branch power constraint:
Pj≤Pj max
wherein, PjIs the active power value of branch j; pj maxThe maximum value is allowed for the active power of branch j.
The above network architecture operation constraints are:
g∈G
wherein g is a reconstructed network topology structure; g is the set of network radial topologies.
The number of switching operations is limited due to the manufacturing process and the difference in current and capacity of the switch. Therefore, in the dynamic reconfiguration process, in addition to satisfying the power flow constraint, the branch capacity constraint, the node voltage constraint and the network topology constraint, the constraint of the number of switching actions must be satisfied, which is shown in the following formula:
wherein, WjmaxThe maximum number of actions of a single switch; wmaxThe maximum number of actions for all switches.
The configuration number constraint of the three-phase unbalance adjustment device aims at a lower-layer model, in optimization variables, the configuration number constraint of the three-phase unbalance adjustment device needs to be considered, and the following constraints are added to constraint conditions:
it should be noted that, in both the first layer model and the second layer model, there are multiple optimization targets, and a decision matrix method is used to realize the conversion from a multi-target function to a single-target function. The multilayer model is solved by mainly adopting a hybrid algorithm based on an ant colony algorithm and cone optimization. In the first layer model, the ant colony algorithm is adopted to rapidly plan the positions of the power distribution network lines and the positions of the interconnection switches, and the cone optimization algorithm is adopted to plan the positions of the reactive compensation equipment and the three-phase imbalance adjusting device; in the second layer model, the cone optimization algorithm is adopted to carry out optimization scheduling on the reactive compensation equipment, the number of groups of three-phase unbalance adjusting devices and the state of the interconnection switch. According to different requirements of different layer models on an algorithm, the multi-layer model is decomposed and calculated, and finally the fast and accurate solution of the large-scale mixed integer nonlinear programming problem is achieved.
Example 2
According to another aspect of the embodiments of the present invention, there is also provided a storage medium, where the storage medium includes a stored program, and where the program is executed to control a device in which the storage medium is located to perform any one of the above methods.
Example 3
According to another aspect of the embodiments of the present invention, there is also provided a processor, configured to execute a program, where the program executes to perform any one of the methods described above.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A power distribution network resource processing method is characterized by comprising the following steps:
acquiring a pre-increased load of the power distribution network;
establishing a target model, wherein the target model is used for evaluating the adjustment needed to be carried out on the resources of the power distribution network under the condition that the power distribution network meets the load requirement;
inputting at least one constraint to the target model, wherein the constraint is indicative of a limited range of a predetermined parameter of the distribution grid if the load requirement is met;
and obtaining a mode for adjusting the resources of the power distribution network according to the target model.
2. The method of claim 1, wherein the target model comprises two layers, wherein a first layer model of the two layers is used to adjust an installation location and/or capacity of a resource in the power distribution grid; the second of the two layers is used to adjust the configuration of resources in the power distribution network.
3. The method of claim 2, further comprising:
the second layer model feeds back the adjustment result of the configuration of the resource to the first layer model as one of the bases for the first layer model to evaluate the installation position and/or capacity adjustment of the resource.
4. The method of claim 2, further comprising:
and the first layer model feeds back the adjustment result of the resource installation position and/or capacity of the first layer model to the second layer model to serve as a constraint condition for the second layer model to adjust the configuration of the resource.
5. The method of any of claims 2 to 4, wherein the first layer model uses the following objective function:
Fupper＝min(ω1f1+ω2f2+ω3f3)
wherein f is1Annual loss charge of distribution line, f2For the efficiency of utilization of the distribution network equipment, f3For operating maintenance costs, omega, of distribution network equipment1、ω2、ω3Are respectively f1、f2、f3The weight occupied.
6. The method of claim 5, wherein the constraints of the first layer model comprise at least one of:
node voltage, power balance, reliability, network topology, investment of distribution lines, wire cross sections, power supply radius, switch allowable action times, investment of switch equipment, investment of reactive compensation devices and investment of three-phase unbalance adjusting devices.
7. The method of any of claims 2 to 4, wherein the first layer model uses the following objective function:
Flower＝min(ω3f3+ω4f4)；
wherein f is3For annual power loss of the distribution line, f4Is the annual voltage deviation minimum, omega3、ω4Are respectively f3、f4The weight occupied.
8. The method of claim 7, wherein the constraints of the second layer model comprise at least one of:
node voltage, power balance, branch power, network topology, the number of allowed actions of the switch, and the number of three-phase imbalance adjusting devices.
9. A storage medium comprising a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method of any one of claims 1 to 8.
10. A processor configured to execute a program, wherein the program executes to perform the method of any one of claims 1 to 8.
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