CN111864742B - Active power distribution system extension planning method and device and terminal equipment - Google Patents
Active power distribution system extension planning method and device and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of urban network planning, and provides an active power distribution system extension planning method, an active power distribution system extension planning device and terminal equipment, wherein the method comprises the following steps: establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side, wherein the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model; randomly coding a route to be expanded in a target planning area according to a construction state to generate a first population; carrying out random coding on the basis of the first population according to the target interrupt load proportion to generate a second population; and solving the optimal particles in the first population and the second population by combining the interruptible load response model, and determining an optimal net rack planning scheme and a corresponding optimal operation scheduling scheme. By the active power distribution system expansion planning method provided by the invention, the solution of the active power distribution system optimal expansion planning scheme considering the load demand response capability of the user side can be realized under the scene of intermittent new energy access.
Description
Technical Field
The invention belongs to the technical field of urban network planning, and particularly relates to an active power distribution system extension planning method, device and terminal equipment.
Background
With the development of new energy, scenes that large-scale intermittent new energy is connected to a power grid are more and more. Due to the intermittent characteristics of new energy sources such as wind energy, solar energy and the like, the supply of the power distribution network has certain uncertainty. In order to adapt to such uncertainty, the load on the user side needs to be adjusted, a demand response mechanism is introduced, and the load on the user side is adjusted to enable the interaction between the user and the power distribution network. The reasonable demand response mechanism can guide the power utilization behavior of users, promote the consumption of the distributed power supply output, and further reduce the construction and operation cost of the power distribution network.
At present, no completely effective planning method exists in the field of power distribution network planning introducing a demand response mechanism, and an optimal power distribution network frame planning scheme and an optimal operation scheduling scheme of a power distribution network are difficult to determine.
Disclosure of Invention
In view of this, embodiments of the present invention provide an active power distribution system extension planning method, an active power distribution system extension planning device, and a terminal device, so as to solve the problem in the prior art that it is difficult to accurately determine a grid planning scheme and an operation strategy of a power distribution network.
A first aspect of an embodiment of the present invention provides an active power distribution system extension planning method, including:
establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
randomly coding a line to be expanded in a target planning region according to a construction state to generate a first population; determining a net rack planning scheme corresponding to each particle in the first population; randomly coding the net rack planning schemes corresponding to the particles in the first population according to the target interrupt load proportion to generate a second population corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme; calculating optimal particles in a second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model; and determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme. A second aspect of the embodiments of the present invention provides an active power distribution system extension planning apparatus, including:
the model establishing module is used for establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
the first group generation module is used for randomly coding the line to be expanded in the target planning area according to the construction state to generate a first group; determining a net rack planning scheme corresponding to each particle in the first population;
the second group generation module is used for randomly coding the net rack planning schemes corresponding to the particles in the first group according to the target interrupt load proportion to generate a second group corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme;
a first optimization module, configured to calculate optimal particles in a second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model;
and the second optimization module is used for determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme.
A third aspect of an embodiment of the present invention provides a terminal device, including: memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the steps of the method as described above are implemented when the processor executes the computer program.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the embodiment of the invention provides an active power distribution system extension planning method, an active power distribution system extension planning device and terminal equipment, wherein the method comprises the following steps: randomly coding a line to be expanded in a target planning region according to a construction state to generate a first population; determining a net rack planning scheme corresponding to each particle in the first population; randomly coding the net rack planning schemes corresponding to the particles in the first population according to the target interrupt load proportion to generate a second population corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme; calculating the optimal particles in the second population corresponding to each net rack planning scheme based on the operation scheduling layer model and the interruptible load response model; and determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme. The technical scheme provided by the embodiment of the invention can fully consider the interruptible load response condition of the user side under the scene of large-scale intermittent new energy access to the power grid, and determine the optimal grid planning scheme and the optimal operation scheduling scheme of the target planning area.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic flow chart of an active power distribution system extension planning method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an active power distribution system extension planning apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Referring to fig. 1, an embodiment of the present invention provides an active power distribution system extension planning method, including:
s101: establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
in the embodiment, the intermittent new energy has strong intermittence and uncertainty, so that when the large-scale intermittent new energy is accessed into a power grid, the power utilization behavior of a user can be reasonably guided through a demand response mechanism, the consumption of the output of the distributed power supply is promoted, and the construction and operation cost of the power distribution network is reduced.
In an embodiment of the present invention, the establishing the interruptible load response model at the user side in S101 includes:
constructing a first objective function corresponding to an interruptible load response model at a user side;
the first objective function is: f ═ F1(Kw) (ii) a Wherein F is the total profit gained by the user in response to the interrupted load request of the distribution network, KwAnd the actual load interruption proportion of the user after the user responds to the load interruption request of the power distribution network.
In this embodiment, the specific form of the first objective function is:
F＝R-C1-C2-F1 (1)
in the formula (1), F represents the total profit obtained by the user in response to the load interruption request of the power distribution network; r represents the direct income obtained by the user in response to the load interruption request of the power distribution network; c1Representing the cost of the loss of power outage paid by the user in response to an interrupted load request on the distribution network, C2Indicating the electric charge required to be paid by the user; f1And the penalty that the actual interrupt load proportion of the user does not reach the target interrupt load proportion is represented.
In this embodiment, when the power supply amount of the large-scale intermittent new energy source of the power distribution network is insufficient, an interrupt load request is sent to the user, and the interrupt load request specifies a target interrupt load proportion of a line corresponding to a load point where the user is located. In order to encourage the user to respond to the load interruption request, the power distribution network gives the user a certain direct benefit; on the other hand, since the actual load reduction amount of the user and the target load reduction amount specified by the interrupt load request are not always completely the same, the user will pay a certain penalty if the user does not reach the specified target load reduction amount. In addition, when the user responds to the load interruption request, the rights and interests of the user can be damaged, and certain power failure loss cost is generated.
In this embodiment, the specific calculation method of R directly benefiting the user from responding to the interrupted load request of the distribution network is as follows:
in the formula (2), Δ PnIndicating the target load reduction amount specified by a power grid company; e is interruption compensation of unit electric quantity; t is tdThe response time is shown, and optionally, in this embodiment, 1h is used as a basic step size for analysis; delta PaRepresenting the actual load reduction of the user.
ΔPn＝Lt×Kiq (3)
In the formula (3), LtIndicating the original load value of the load point at which the user was located at the moment of response,the data are acquired; kiqAnd representing the target interrupt load proportion of the ith line in the current operation scheduling scheme specified by the power distribution network.
ΔPa＝Lt×Kw (4)
In the formula (4), LtThe original load value of the load point of the user at the response moment is obtained through data acquisition; kwRepresenting the user's actual interrupt load proportion.
In this embodiment, the power outage loss cost C paid by the user in response to the load interruption request of the distribution network1The specific calculation method is as follows:
C1＝(K1ΔPa 2+K2ΔPa)td (5)
in the formula (5), K1And K2For constant coefficient, optional, K1＝500，K2＝1000。
The electricity fee C that the user needs to pay in the embodiment2The specific calculation method is as follows:
C2＝αp(Lt-ΔPa)td (6)
in the formula (6), α represents the discount of the electricity price of the remaining load after the load reduction; and p is the electricity price.
In this embodiment, the specific penalty calculation method for the user that the actual interrupt load proportion does not reach the target interrupt load proportion is as follows:
in the formula (7), pfIndicating the penalty of the user's unit difference power consumption when the specified reduction is not completed.
In this embodiment, E, α and pfIs a preset constant.
In an embodiment of the present invention, the building a power distribution network double-layer optimization model in S101 includes:
constructing a second objective function corresponding to the net rack planning layer model;
and the second objective function takes the construction or non-construction of each line to be expanded as an independent variable and takes the cost corresponding to each net rack planning scheme as a dependent variable.
In this embodiment, the network frame planning layer model optimizes whether each line to be expanded is constructed or not by taking the minimum comprehensive cost of construction and operation of the power distribution network as a target.
In this embodiment, the second objective function is:
F2＝Finv+Fm+Fun+FDR+Floss-Fge (8)
in the formula (8), F2Indicating the construction and operation costs of the distribution network, i.e. the cost of the distribution network, FinvEqual annual value of grid construction cost, FmRepresenting the operation and maintenance costs of the grid, FunRepresenting cost of wind and light abandoning, FDRIndicating the demand response charge of the distribution network, FlossRepresenting the loss of the network, FgeRepresenting a distributed power generation subsidy.
In this embodiment, the equal annual value F of the grid construction costinvThe specific calculation method is as follows:
in the formula (9), r represents the discount rate, m represents the life cycle of the line, and the discount rate and the life cycle of the line are obtained according to historical data; n is the number of lines to be expanded, betal.iIs a 0/1 variable, which indicates the commissioning status of the line i to be extended in the extended planning scheme l, specifically betal.i0, denotes that the corresponding line is not established, βl.1, representing that the corresponding line is constructed; ciAnd the construction cost of the line i is calculated according to the actual line length and the unit line construction cost.
In this embodiment, the operation and maintenance cost F of the rackmThe specific calculation method is as follows:
Fm＝μFinv (10)
in the formula (10), μ represents a net rack operation and maintenance cost ratio and is obtained from historical data.
In this embodiment, the wind and light abandoning cost FunThe specific calculation method is as follows:
in the formula (11), S represents the operation scene set, omega, of the power distribution networkSThe number of days of the scene s is shown, and the operation scene of the power distribution network can be obtained through historical data statistics. CunAnd the cost of wind and light abandonment of a unit is shown.
The maximum output of the distributed power supply at the t moment in the scene s is determined by factors such as a fan, light intensity and the like; pDG,s,tAnd the actual output of the distributed power supply at the time t in the scene of s is shown.
In the embodiment, the demand response fee F of the power distribution networkDRThe specific calculation method is as follows:
in the formula (12), S represents an operation scene set, omega, of the power distribution networkSRepresents the number of days that scene s appears; fjtAnd the load response cost required to be paid by the power distribution company when the load point j is actively reduced at the time t is shown.
Fjt＝R(j)-F(j)+(1-α)×(Lj(t)-ΔPa(j))×td×p (13)
In the formula (13), R (j) and F (j) represent the profit and penalty of the load point j in response, Lj(t) represents the real-time load at time t of load point j, Δ Pa(j) The user actual reduction amount of the load point j is shown, and the electricity price book of the residual load after the load reduction is shown by alphaBuckling; and p is the electricity price.
In the present embodiment, the loss of the power distribution network is FlossThe specific calculation method is as follows:
in the formula (14), ε represents the unit loss cost, Ploss,s,tRepresenting the value of the network loss for the t period in the s scenario.
In the present embodiment, the distributed power generation patch FgeThe specific calculation method is as follows:
in the formula (15), CDGAnd representing the unit power generation amount subsidy of the distributed power supply.
In this embodiment, the net rack planning scheme corresponding to the net rack planning layer model further needs to satisfy the feeder capacity constraint.
The feeder capacity constraints are:
Si<Si,max (16)
in the formula (16), SiRepresenting the actual load factor, S, of the line ii，maxRepresenting the maximum load rate of feeder i.
In an embodiment of the present invention, S101 includes constructing a third objective function corresponding to the running scheduling layer model;
the third objective function takes the target interrupt load proportion corresponding to each line in the target planning area as an independent variable and takes the operation scheduling cost corresponding to each target interrupt load proportion as a dependent variable.
In this embodiment, the specific form of the third objective function is:
F3＝Fun+FDR+Floss-Fge (17)
in the formula (17), F3To run the scheduling cost, FunRepresenting cost of wind and light abandoning, FDRIndicating the demand response charge of the distribution network, FlossRepresenting the loss of the network, FgeRepresenting a distributed power generation subsidy.
Since the operation scheduling cost of the power distribution network is a part of the cost of the power distribution network, the specific calculation method of the third objective function is already given in the specific calculation method part of the second objective function.
In this embodiment, the operation scheduling scheme of the power distribution network needs to satisfy the power balance constraint, the DG output constraint, and the node voltage constraint.
The specific form of the power balance constraint is:
in the formula (18), Pi,t,s、Qi,t,sRepresenting the active power and reactive power, U, injected at the node i at the time t under the scene si,t,s、Uj,t,sRepresenting the voltage amplitudes at the node i and the node j at the moment t under the scene s; gijAnd BijRespectively representing the conductance and susceptance of the line ij; thetaij,t,sRepresenting the voltage phase angle difference between node i and node j at time t under scene s.
The specific form of DG output constraint is:
in the formula (19), PDG,i,t,sAnd QDG,i,t,sRespectively representing active power output and reactive power output of DGs at a node i point at the t moment in an s scene; pDG,minAnd QDG,minRespectively representing the minimum value of the DG active power and reactive power; pDG,maxAnd QDG,maxRespectively representing the maximum value of the DG active power and reactive power.
The specific form of the node voltage constraint is as follows:
Umin≤Ui,t,s≤Umax (20)
in formula (20), UminAnd UmaxRepresenting the upper and lower limits of the voltage amplitude of the distribution network node.
S102: randomly coding a line to be expanded in a target planning region according to a construction state to generate a first population; determining a net rack planning scheme corresponding to each particle in the first population;
in this embodiment, the grid planning scheme corresponding to the grid planning layer model must meet the connectivity and radiancy requirements, and each load point should exist in a power supply path connected to the upper-level power grid, so that the situation of an isolated node, an isolated island or a closed loop cannot occur.
In an embodiment of the present invention, in S102, randomly encoding a line to be expanded in a target planning region according to a construction state, and generating a first population, includes:
acquiring an existing line set, lines to be expanded and construction cost of each line to be expanded in the target planning region;
calculating the weight corresponding to each line to be expanded according to the construction cost of each line to be expanded;
judging whether each line to be expanded can form a loop with the lines in the existing line set, if the first line to be expanded cannot form a loop with the lines in the existing line set, adding the first line to be expanded into the existing line set to obtain an existing line updating set; if the first line to be expanded can form a loop with the line in the existing line set, adding the first line to be expanded into a coding line set; the first line to be expanded is any line to be expanded;
randomly encoding the encoding line set to generate a first group; the particles in the first population are x ═ x1,x2,…,xN]；xiRepresenting the construction state of the ith line to be expanded in the coding line set; and xi0/1; and N is the number of lines to be expanded in the coding line set.
In an embodiment of the present invention, the determining the grid planning scheme corresponding to each particle in the first population in S102 includes:
constructing a first spanning tree based on the lines in the existing line updating set;
adding a line to be expanded corresponding to an element with a code value of 1 in a first particle into the first spanning tree, and updating the first spanning tree; the first particle is any particle in the first population;
deleting the line to be expanded with the maximum weight except the newly added line to be expanded in the first loop to form a second spanning tree; the first loop is any loop in the updated first spanning tree;
taking an element in the first particle corresponding to a line to be expanded in the second spanning tree as a first element, marking a line construction state corresponding to the first element as construction, and marking a line construction state corresponding to an element except the first element in the first particle as non-construction; and determining a net rack planning scheme corresponding to the first particle.
In the present embodiment, xi0 means that the ith line to be expanded in the coding line set is not constructed, and xiAnd 1 represents that the ith line to be expanded in the coding line set is constructed.
In the present embodiment, the line planning state β of each planning plan in the second objective function, equation (9), is determined by the above methodl,iThe value of (a). Specifically, if the construction state corresponding to the element in the first particle corresponding to the line to be expanded in the second spanning tree is construction, the corresponding β isl,i1, the rest of betal,i＝0。
The coding method and the method for determining the net rack planning scheme provided by the embodiment can reduce the occurrence of infeasible solutions in the optimizing process of the net rack planning scheme.
S103: randomly coding the net rack planning schemes corresponding to the particles in the first population according to the target interrupt load proportion to generate a second population corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme;
in one embodiment of the present invention, S103 includes:
acquiring a net rack planning scheme corresponding to each first group of particles;
coding the target interrupt load proportion of each line corresponding to each net rack planning scheme to obtain a second code;
the encoding mode of the second encoding is as follows: k ═ K1q,K2q,…KAq]；Kiq∈[0,1],KiqThe value of represents the target interrupt load proportion corresponding to the ith line in the current net rack planning scheme; a is the number of lines included in the current net rack planning scheme;
and randomly coding the second codes to generate a second population running the scheduling scheme codes.
In this embodiment, one particle in the second population corresponding to the first grid plan corresponds to one operation scheduling plan.
S104: calculating optimal particles in a second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model;
in one embodiment of the present invention, S104 includes:
initializing the speed and position of each second population of particles in the second population;
calculating the actual user interrupt load proportion of each operation scheduling scheme corresponding to the first grid planning scheme according to an interruptible load response model at the user side; the first net rack planning scheme is any net rack planning scheme;
calculating a particle adaptive value of a second population corresponding to the first grid planning scheme based on the actual interruption load proportion of the user of each operation scheduling scheme corresponding to the first grid planning scheme and the operation scheduling layer model;
and updating the second population corresponding to the first grid planning scheme until the particle adaptation value of the second population corresponding to the first grid planning scheme converges or reaches the maximum iteration number, so as to obtain the optimal particles in the second population corresponding to the first grid planning scheme.
In this embodiment, the user actual interrupt load proportion of each operation scheduling scheme corresponding to the first rack planning scheme is calculated by using the first objective function corresponding to the interruptible load response model at the user side.
Specifically, in the first objective function, namely equation (1), after the interruption load proportion of the power distribution network is determined, the corresponding actual user reduction proportion is solved under the condition that the maximum total profit obtained by a user in response to the interruption request of the power distribution network is a preset value.
Optionally, an fmincon solver is used to solve the user actual reduction ratio.
In this embodiment, after the actual reduction ratio of the user is determined, a third objective function, that is, equation (17), is solved to obtain an operation scheduling cost corresponding to the first operation scheduling scheme, and then a particle adaptive value of the second population corresponding to the first grid planning scheme may be calculated, where the first operation scheduling scheme is any one of the operation scheduling schemes corresponding to the first grid planning scheme.
S105: and determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme.
In one embodiment of the present invention, S105 includes:
initializing the speed and position of each first population of particles in the first population;
calculating a particle adaptive value of the first population based on the optimal particles in the second population corresponding to each net rack planning scheme and the net rack planning layer model;
and updating the first population until the particle adaptation value of the first population converges or the maximum iteration times is reached, and determining an optimal net rack planning scheme and a corresponding optimal operation scheduling scheme.
In this embodiment, based on the grid planning scheme and the corresponding optimal operation scheduling scheme, the second objective function, that is, equation (8), is solved to obtain the first grid planning scheme and the comprehensive cost of construction and operation of the power distribution network under the corresponding optimal operation scheduling scheme, that is, the cost, and further, the particle adaptation value of the first population can be calculated.
By the active power distribution system extension planning method provided by the embodiment of the invention, the optimal grid planning scheme considering the load demand response capability of the user side and the solution of the optimal operation scheduling scheme of the response can be realized under the scene with intermittent new energy access on the premise of reducing the operation amount and avoiding infeasible solution.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Referring to fig. 2, an embodiment of the present invention provides an active power distribution system extension planning apparatus, including:
the model establishing module is used for establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
the first group generation module is used for randomly coding the line to be expanded in the target planning area according to the construction state to generate a first group; determining a net rack planning scheme corresponding to each particle in the first population;
the second group generation module is used for randomly coding the net rack planning schemes corresponding to the particles in the first group according to the target interrupt load proportion to generate a second group corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme;
a first optimization module, configured to calculate optimal particles in the second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model
And the second optimization module is used for determining an optimal scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme.
The active power distribution system extension planning device provided by the embodiment of the invention can realize the solution of the optimal grid planning scheme and the optimal operation scheduling scheme of response considering the load demand response capability of the user side under the scene with intermittent new energy access on the premise of reducing the operation amount and avoiding infeasible solution.
In this embodiment, the model building module includes:
the interruptible load response model establishing unit is used for establishing a first objective function corresponding to the interruptible load response model at the user side;
the first objective function is: f ═ F1(Kw) (ii) a Wherein F is the total profit gained by the user in response to the interrupted load request of the distribution network, KwThe actual load interruption proportion of the user after the user responds to the load interruption request of the power distribution network is given;
the net rack planning layer model building unit is used for building a second objective function corresponding to the net rack planning layer model;
the second objective function takes the construction or non-construction of each line to be expanded as an independent variable, and takes the cost corresponding to each net rack planning scheme as a dependent variable;
the network frame planning layer model establishing unit is used for establishing a third objective function corresponding to the operation scheduling layer model;
and the third objective function takes the target interrupt load proportion corresponding to each line in the target planning area as an independent variable and takes the operation scheduling cost corresponding to each target interrupt load proportion as a dependent variable.
In this embodiment, the first population generating module includes:
the first acquisition unit is used for acquiring the existing line set, the lines to be expanded and the construction cost of each line to be expanded in the target planning area;
the weight calculation unit is used for calculating the weight corresponding to each line to be expanded according to the construction cost of each line to be expanded;
the first judgment unit is used for judging whether each line to be expanded can form a loop with the lines in the existing line set;
the existing line updating set generating unit is used for adding the first line to be expanded into the existing line set to obtain an existing line updating set if the first line to be expanded cannot form a loop with the line in the existing line set;
the coding line set generating unit is used for adding the first line to be expanded into the coding line set if the first line to be expanded can form a loop with the lines in the existing line set; the first line to be expanded is any line to be expanded.
A first group generating unit, configured to randomly encode the encoding line set to generate a first group; the particles in the first population are x ═ x1,x2,…,xN]；xiRepresenting the construction state of the ith line to be expanded in the coding line set; and xi0/1; and N is the number of lines to be expanded in the coding line set.
The first spanning tree generating unit is used for constructing a first spanning tree based on the lines in the existing line updating set;
the first spanning tree updating unit is used for adding the line to be expanded corresponding to the element with the code value of 1 in the first particle into the first spanning tree and updating the first spanning tree; the first particles are any one of a first population of particles;
the second spanning tree generating unit is used for deleting the line to be expanded with the maximum weight except the newly added line to be expanded in the first loop to form a second spanning tree; the first loop is any loop in the updated first spanning tree;
the network frame planning scheme determining unit is used for taking elements in first particles corresponding to the lines to be expanded in the second spanning tree as first elements, marking the line construction states corresponding to the first elements as construction, and marking the line construction states corresponding to the elements except the first elements in the first particles as non-construction; and determining a net rack planning scheme corresponding to the first particle.
The second population generation module comprises:
the second obtaining unit is used for obtaining the net rack planning scheme corresponding to each particle in the first group;
second population generation sheetThe element is used for randomly coding the target interrupt load proportion of each line corresponding to the first net rack planning scheme to obtain a second population corresponding to the first net rack planning scheme; the first net rack planning scheme is any net rack planning scheme; the second population corresponding to the first net rack planning scheme is as follows: kq＝[K1q,K2q,…KAq]；Kiq∈[0,1]；KiqThe value of represents the target interrupt load proportion corresponding to the ith line in the net rack planning scheme q; and A is the number of lines included in the net rack planning scheme q.
The first optimization module includes:
a first initializing unit for initializing the speed and position of each particle in the second population;
the user actual interrupt load proportion calculation unit is used for calculating the user actual interrupt load proportion of each operation scheduling scheme corresponding to the first net rack planning scheme according to the interruptible load response model at the user side; the first net rack planning scheme is any net rack planning scheme;
a first adaptive value calculating unit, configured to calculate, based on the actual interrupt load ratios of the users of the respective operation scheduling schemes corresponding to the first grid planning scheme and the operation scheduling layer model, the adaptive values of the particles of the second population corresponding to the first grid planning scheme
And the first updating unit is used for updating the second population until the particle adaptation value of the second population converges or reaches the maximum iteration times to obtain the optimal particles in the second population corresponding to the first grid planning scheme.
The second optimization module includes:
a second initializing unit, configured to initialize a speed and a position of each particle in the first population;
a second adaptive value calculating unit, configured to calculate an adaptive value of the particles of the first population based on the optimal particles in the second population corresponding to each grid planning scheme and the grid planning layer model;
and the second updating unit is used for updating the first population until the adaptive values of the particles in the first population converge or reach the maximum iteration times, and determining an optimal net rack planning scheme and a corresponding optimal operation scheduling scheme.
Fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 3, the terminal device 3 of this embodiment includes: a processor 30, a memory 31 and a computer program 32 stored in said memory 31 and executable on said processor 30. The processor 30, when executing the computer program 32, implements the steps in the various embodiments of the active power distribution system extension planning method described above, such as the steps 101-104 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of each module/unit in the above-mentioned device embodiments, such as the functions of the modules 110 to 140 shown in fig. 2.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3.
The terminal device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 30, a memory 31. It will be understood by those skilled in the art that fig. 3 is only an example of the terminal device 3, and does not constitute a limitation to the terminal device 3, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device may also include an input-output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used for storing the computer program and other programs and data required by the terminal device. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of 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, devices or units, and may be in an electrical, mechanical 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 network 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 modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. An active power distribution system extension planning method is characterized by comprising the following steps:
establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
randomly coding a line to be expanded in a target planning region according to a construction state to generate a first population; determining a net rack planning scheme corresponding to each particle in the first population; randomly coding the net rack planning schemes corresponding to the particles in the first population according to the target interrupt load proportion to generate a second population corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme; calculating optimal particles in a second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model; determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme;
the randomly coding the line to be expanded in the target planning region according to the construction state to generate a first population, and the method comprises the following steps:
acquiring an existing line set, lines to be expanded and construction cost of each line to be expanded in the target planning region;
calculating the weight corresponding to each line to be expanded according to the construction cost of each line to be expanded;
judging whether each line to be expanded can form a loop with the lines in the existing line set, if the first line to be expanded cannot form a loop with the lines in the existing line set, adding the first line to be expanded into the existing line set to obtain an existing line updating set; if the first line to be expanded can form a loop with the line in the existing line set, adding the first line to be expanded into a coding line set; the first line to be expanded is any line to be expanded;
randomly encoding the encoding line set to generate a first group; the particles in the first population are x ═ x1,x2,…,xN]；xiRepresenting the construction state of the ith line to be expanded in the coding line set; and xi0/1; n is the weaveThe number of lines to be extended in the code line set.
2. The active power distribution system reach planning method of claim 1, wherein the establishing the customer-side interruptible load response model comprises:
constructing a first objective function corresponding to an interruptible load response model at a user side;
the first objective function is: f ═ F1(Kw) (ii) a Wherein F is the total profit gained by the user in response to the interrupted load request of the distribution network, KwAnd the actual load interruption proportion of the user after the user responds to the load interruption request of the power distribution network.
3. The method for planning the expansion of an active power distribution system according to claim 1, wherein the establishing a power distribution network double-layer optimization model comprises:
constructing a second objective function corresponding to the net rack planning layer model;
and the second objective function takes the construction or non-construction of each line to be expanded as an independent variable and takes the cost corresponding to each net rack planning scheme as a dependent variable.
4. The method for planning the expansion of an active power distribution system according to claim 1, wherein the establishing a power distribution network double-layer optimization model comprises:
constructing a third objective function corresponding to the operation scheduling layer model;
and the third objective function takes the target interrupt load proportion corresponding to each line in the target planning area as an independent variable and takes the operation scheduling cost corresponding to each target interrupt load proportion as a dependent variable.
5. The active power distribution system extension planning method of claim 1, wherein the determining the grid planning solution corresponding to each particle in the first population comprises:
constructing a first spanning tree based on the lines in the existing line updating set;
adding a line to be expanded corresponding to an element with a code value of 1 in a first particle into the first spanning tree, and updating the first spanning tree; the first particle is any particle in the first population;
deleting the line to be expanded with the maximum weight except the newly added line to be expanded in the first loop to form a second spanning tree; the first loop is any loop in the updated first spanning tree;
taking an element in the first particle corresponding to a line to be expanded in the second spanning tree as a first element, marking a line construction state corresponding to the first element as construction, and marking a line construction state corresponding to an element except the first element in the first particle as non-construction; and determining a net rack planning scheme corresponding to the first particle.
6. The active power distribution system extension planning method according to claim 1, wherein the randomly encoding the rack planning schemes corresponding to the particles in the first population according to the target interrupt load ratio to generate the second population corresponding to each rack planning scheme comprises:
acquiring a net rack planning scheme corresponding to each particle in the first population;
randomly coding the target interrupt load proportion of each line corresponding to the first net rack planning scheme to obtain a second population corresponding to the first net rack planning scheme; the first net rack planning scheme is any net rack planning scheme; the second population corresponding to the first net rack planning scheme is as follows: kq＝[K1q,K2q,…KAq]；Kiq∈[0,1]；KiqThe value of represents the target interrupt load proportion corresponding to the ith line in the net rack planning scheme q; and A is the number of lines included in the net rack planning scheme q.
7. The active power distribution system extension planning method according to claim 1, wherein the calculating optimal particles in the second population corresponding to each grid planning solution based on the operation scheduling layer model and the interruptible load response model comprises:
initializing the speed and position of each particle in the second population;
calculating the actual user interrupt load proportion of each operation scheduling scheme corresponding to the first grid planning scheme according to an interruptible load response model at the user side; the first net rack planning scheme is any net rack planning scheme;
calculating a particle adaptive value of a second population corresponding to the first grid planning scheme based on the actual interruption load proportion of the user of each operation scheduling scheme corresponding to the first grid planning scheme and the operation scheduling layer model;
and updating the second population corresponding to the first grid planning scheme until the particle adaptation value of the second population corresponding to the first grid planning scheme converges or reaches the maximum iteration number, so as to obtain the optimal particles in the second population corresponding to the first grid planning scheme.
8. The active power distribution system extension planning method according to claim 1, wherein the determining an optimal grid planning scheme and an optimal operation scheduling scheme based on the grid planning layer model and the optimal particles in the second population corresponding to each grid planning scheme comprises:
initializing the speed and position of each particle in the first population;
calculating a particle adaptive value of the first population based on the optimal particles in the second population corresponding to each net rack planning scheme and the net rack planning layer model;
and updating the first population until the adaptive values of the particles in the first population converge or reach the maximum iteration times, and determining an optimal net rack planning scheme and a corresponding optimal operation scheduling scheme.
9. An active power distribution system extension planning apparatus, comprising:
the model establishing module is used for establishing a power distribution network double-layer optimization model and an interruptible load response model at a user side; the power distribution network double-layer optimization model comprises a net rack planning layer model and an operation scheduling layer model;
the first group generation module is used for randomly coding the line to be expanded in the target planning area according to the construction state to generate a first group; determining a net rack planning scheme corresponding to each particle in the first population;
the second group generation module is used for randomly coding the net rack planning schemes corresponding to the particles in the first group according to the target interrupt load proportion to generate a second group corresponding to each net rack planning scheme; each particle in the second population corresponds to one operation scheduling scheme;
a first optimization module, configured to calculate optimal particles in a second population corresponding to each grid planning scheme based on the operation scheduling layer model and the interruptible load response model;
the second optimization module is used for determining an optimal net rack planning scheme and an optimal operation scheduling scheme based on the net rack planning layer model and the optimal particles in the second population corresponding to each net rack planning scheme;
the first population generating module comprises:
the first acquisition unit is used for acquiring the existing line set, the lines to be expanded and the construction cost of each line to be expanded in the target planning area;
the weight calculation unit is used for calculating the weight corresponding to each line to be expanded according to the construction cost of each line to be expanded;
the first judgment unit is used for judging whether each line to be expanded can form a loop with the lines in the existing line set;
the existing line updating set generating unit is used for adding the first line to be expanded into the existing line set to obtain an existing line updating set if the first line to be expanded cannot form a loop with the line in the existing line set;
the coding line set generating unit is used for adding the first line to be expanded into the coding line set if the first line to be expanded can form a loop with the lines in the existing line set; the first line to be expanded is any one line to be expanded;
a first group generating unit, configured to randomly encode the encoding line set to generate a first group; the particles in the first population are x ═ x1,x2,…,xN]；xiRepresenting the construction state of the ith line to be expanded in the coding line set; and xi0/1; and N is the number of lines to be expanded in the coding line set.
10. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 8 when executing the computer program.
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