CN107392418B - Urban power distribution network reconstruction method and system - Google Patents
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Abstract
The invention discloses a method and a system for reconstructing an urban power distribution network. The method comprises the following steps: establishing a multi-objective optimization function with minimum annual power supply shortage amount, average system outage duration, average system outage frequency and network loss of the urban power distribution network; determining constraint conditions of the multi-objective optimization functions and the weight of each objective optimization function; setting an initial on-off value of a switch of each load node in the urban power distribution network; solving a multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the on-off initial value of the switch of each load node, the multi-objective optimization function and constraint conditions; and reconstructing the network of the urban distribution network according to the on-off of the switch of each load node in the urban distribution network corresponding to the solution of the weighted sum minimum multi-objective optimization function. The invention can optimize the network loss of the urban distribution network containing the distributed power supply and the automobile charging station, reduce the operation cost and improve the quality of the power supply voltage of the urban distribution network.
Description
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a method and a system for reconstructing an urban power distribution network.
Background
In recent years, with the rapid development of urban economy, the demand for electric energy is rising, the power load is increased year by year, the structure of the urban distribution network is increasingly complex, and the network loss of the urban distribution network is increased year by year. Meanwhile, in recent years, the generated energy of various energy sources in China can not meet the electricity utilization requirements of people, the switching-off electricity limiting phenomenon is obvious day by day, the contradiction between supply and demand is intensified day by day, and the influence on the urban economic development and the quality of life of people is intensified year by year. In order to solve the problems, the state is greatly promoting the intelligent construction and transformation of the urban power distribution network, and along with the gradual access of large-scale distributed energy and electric vehicle charging stations to the urban power distribution network, the urban power distribution network has the characteristics of large scale, more nodes, miscellaneous equipment, more operation modes and the like. There is a need for some way to improve the economics, reliability and safety of operation of urban power distribution networks.
Network reconfiguration is an important means for improving the running economy, power supply reliability and safety of the urban power distribution network. The urban distribution network is planned and adjusted in system structure through network reconstruction, so that the urban distribution network is more economical, and the network has better electric energy quality. However, the current method for reconstructing the urban distribution network has relatively few considerations for new energy and new load access. Meanwhile, the existing urban distribution network reconstruction algorithm has certain problems, for example, the binary coding-based particle swarm algorithm is easy to fall into local convergence during particle swarm analysis, a large number of non-feasible solutions are generated, the calculation amount is huge, the consumed time is long, the solution has very large limitations, and the increasingly complex urban distribution network is difficult to deal with.
Disclosure of Invention
The embodiment of the invention provides a method and a system for reconstructing a network of an urban power distribution network, which are beneficial to economic, safe and reliable operation of the urban power distribution network aiming at the urban power distribution network accessed by novel loads such as distributed energy and electric vehicles.
In a first aspect, a method for reconstructing a network of an urban distribution network is provided, including: establishing a multi-objective optimization function with minimum annual power supply shortage amount, average system outage duration, average system outage frequency and network loss of the urban power distribution network; determining constraint conditions of the multi-objective optimization functions and the weight of each objective optimization function; setting an initial on-off value of a switch of each load node in the urban power distribution network; solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the initial on-off value of the switch of each load node, the multi-objective optimization function and the constraint condition; and reconstructing the urban distribution network according to the on-off of the switch of each load node in the urban distribution network corresponding to the solution of the weighted sum of the minimum multi-objective optimization functions.
In a second aspect, a system for reconstructing a network of an urban power distribution network is provided, which includes: the system comprises an establishing module, a judging module and a control module, wherein the establishing module is used for establishing a multi-objective optimization function with the minimum annual power shortage amount of the system, the average outage duration time of the system, the average outage frequency of the system and the minimum network loss of the urban power distribution network; the determining module is used for determining the constraint conditions of the multi-objective optimization functions and the weight of each objective optimization function; the setting module is used for setting an initial on-off value of a switch of each load node in the urban power distribution network; the solving module is used for solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the initial on-off value of the switch of each load node, the multi-objective optimization function and the constraint condition; and the reconstruction module is used for reconstructing the urban distribution network according to the on-off state of the switch of each load node in the urban distribution network corresponding to the solution of the multi-objective optimization function with the minimum weighted sum.
Therefore, the embodiment of the invention can optimize the network loss of the urban distribution network containing the distributed power supply and the automobile charging station, reduce the operation cost and improve the quality of the power supply voltage of the urban distribution network.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced 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 that other drawings can be obtained according to these drawings without inventive labor.
Fig. 1 is a flowchart of a method for reconstructing a network of an urban distribution network according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of solving a multi-objective optimization function using a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to an embodiment of the present invention;
FIG. 3 is a flow chart of the steps of updating individual optimal particles of an embodiment of the present invention;
FIG. 4 is a flowchart of the steps of updating globally optimal particles according to an embodiment of the present invention;
fig. 5 is a block diagram of a network reconfiguration system of an urban distribution network according to an embodiment of the present invention;
FIG. 6 is a block diagram of an IEEE69 node power distribution network in accordance with an embodiment of the present invention;
fig. 7 is a diagram illustrating the effect of the voltages of the nodes before and after the network reconfiguration in the IEEE69 node power distribution network according to the embodiment of the present invention.
Detailed Description
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 some, not all, embodiments of the present invention. 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.
The embodiment of the invention discloses a network reconstruction method for an urban power distribution network, and aims to provide a multi-objective optimization network reconstruction method for the urban power distribution network, which is provided under the new scene that large-scale distributed energy and charging stations are accessed into the urban power distribution network and takes operation cost, power supply reliability and power quality into consideration. As shown in fig. 1, the method comprises the steps of:
the particles in the embodiment of the invention are all switch state sets (0 or 1) in the power distribution network, the positions of the particles are corresponding switch states of the power distribution network, and the speed of the particles is corresponding switch state correction quantity of the power distribution network.
Step S10: and establishing a multi-objective optimization function with the minimum annual power supply shortage amount of the system, the average outage duration time of the system, the average outage frequency of the system and the minimum network loss of the urban power distribution network.
Wherein the content of the first and second substances,the objective optimization function of the annual power shortage amount of the system is as follows:
the objective optimization function for the average outage duration of the system is:
the objective optimization function of the average outage frequency of the system is as follows:
wherein i is the load node in the system, m is the sum of the load nodes in the system, and La(i)Is the average load of load node i, ΨiIs the annual average outage time, λ, of the load node iiIs the annual average outage frequency, S, of the load node iiIs the apparent power of the load node i.
The objective optimization function with the minimum network loss of the urban power distribution network is as follows:
wherein f is1For system network loss, L is the number of system branches, kiIs a switch state variable, 0 represents open, 1 represents closed, riIs a branch i resistance, PiAnd QiRespectively the active and reactive power, V, flowing through the i-end of the branchiIs the branch end node voltage.
Step S20: the constraints of the multi-objective optimization function and the weight of each objective optimization function are determined.
Preferably, the constraints include: a power distribution network topology constraint, a node power balance constraint, a line power constraint, a distributed power supply power constraint, a node voltage constraint, and a node current constraint.
Specifically, the power distribution network topology constraints include: and G belongs to G, wherein G is a network topological structure found by a power distribution network reconstruction target, and G is all topological structures meeting network constraint conditions.
The node power balance constraints include:
and
wherein, PisAnd QisRespectively performing active injection and reactive injection on the node i; u shapeiIs the voltage amplitude of node i; j belongs to i and represents that the node j is connected with the node i; gijAnd BijRespectively a real part and an imaginary part of the node admittance matrix; thetaijIs the voltage phase angle difference between nodes i and j.
The line power constraints include: si＜SimaxI is 1, 2, … …, n, wherein SiFor the actual transmission power of the line, SimaxN is the maximum capacity of the transport of line i and is the number of nodes.
Distributed power supply power constraints include:
wherein S isDGi,maxIs the maximum apparent power, P, of the ith distributed power supplyDGiActive power, Q, for the ith distributed power supplyDGiIs the reactive power of the ith distributed power supply.
The node voltage constraints include: u shapeimin≤Ui≤UimaxI is 1, 2, … …, m, wherein UiminFor node i minimum allowed voltage, UimaxThe maximum allowable voltage of a node i is defined, and m is the number of nodes;
the power saving current constraint comprises: i isimin≤Ii≤IimaxI is 1, 2, … …, m, wherein, IiminMinimum allowable Current for node I, IimaxThe maximum allowable current of the node i, and m is the number of nodes.
The weight setting method comprises the following steps: the ratio of the influence of each objective function on the optimization expectation after the objective functions are combined is empirically determined, for example, the weight of the objective optimization function of the annual power supply shortage amount of the system is 0.1, the weight of the objective optimization function of the system average power failure duration is 0.3, the weight of the objective optimization function of the system average power failure frequency is 0.4, the weight of the objective optimization function of the minimum network loss of the urban distribution network is 0.2, and then the total objective function value is obtained by adding the solution of each objective function value multiplied by the corresponding weight in step S40.
Step S30: and setting an initial on-off value of a switch of each load node in the urban power distribution network.
Through the steps, the initial on-off value of the switch of each load node in the urban power distribution network is preset. According to each preset result, the multi-objective optimization function can be solved by adopting the subsequent steps.
Step S40: and solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the on-off initial value of the switch of each load node, the multi-objective optimization function and the constraint conditions.
Specifically, step S40 may be to solve the target optimization parameters based on a multi-target particle swarm optimization algorithm of decomposition and differential evolution. As shown in fig. 2, the specific steps of the algorithm are as follows:
step S401: inputting initial information of the urban distribution network, and setting the particle swarm size.
The initial information of the urban distribution network comprises parameters required for solving the multi-objective optimization function.
Step S402: and generating N inertia weight vectors according to the homogenization direction vector, and setting iteration times.
The step is equivalent to setting the weight of each objective optimization function, namely, converting the multi-objective optimization problem of N objective optimization functions into the weighted summation problem of N single objective function optimization problems.
Step S403: and updating the individual optimal particles.
Specifically, as shown in fig. 3, step S403 includes the following processes:
step S4031: and inputting particle swarm information.
Step S4032: and carrying out load flow calculation.
The target optimization function value of the current state can be obtained through the steps.
After the automobile charging station and the distributed power supply are merged into the power distribution network, the distributed power supply DG can be represented by three types of nodes according to the access operation condition of the automobile charging station and the distributed power supply in the network: PQ constant nodes, PV constant nodes, and PI constant nodes. When power distribution network load flow calculation is carried out, corresponding mathematical models need to be constructed aiming at different node types.
(1) PQ-constant DG model
At present, a newly built grid-connected wind turbine generator mostly uses synchronous direct-drive and double-fed induction generators, and compared with loads with equal power, the DGs only have opposite power flow directions, so that the DGs can be regarded as PQ constant nodes. When the distributed power supply is a PQ constant node, the model of load flow calculation is as follows:
wherein, P1sAnd Q1sThe active power and the reactive power of the PQ constant type DG are respectively.
(2) PV constant DG model
The output voltage of the automobile charging station and the energy storage battery which are connected in a grid is constant, the output active power is controllable, and the energy storage device can be used as a load to store electric energy in the power grid and can also be used as a distributed power supply to supply power to the power grid. When the energy storage device works in a rectification state, the energy storage device is in a charging state, and energy flows from the power grid side to the direct current side; when the inverter works in an inversion state, the energy storage device is in a discharge state, and the energy on the direct current side is fed back to the power grid. It can therefore be considered a PV constant type node. When the distributed power supply is a PV constant node, the model of load flow calculation of the energy storage battery in a discharging state is as follows:
wherein, P2sAnd V2sRespectively the active power and voltage of the PV constant DG.
The model for the load flow calculation of the energy storage battery in the charging state and the charging state of the electric automobile is as follows:
wherein, P3sAnd V3sThe active power and the voltage of the energy storage battery are respectively charged for the electric automobile and in a charging state.
(3) PI constant DG model
The photovoltaic grid connection mostly adopts a voltage source type current control inverter. Therefore, when load flow calculation is carried out, the photovoltaic is regarded as a PI constant type node. When the distributed power supply is a PI constant node, the model of photovoltaic load flow calculation is as follows:
wherein, P4sAnd I4sRespectively the active power and the current of the PI constant DG.
Step S4033: the particle velocity and position are updated.
The particle velocity and position are updated separately according to the following equations:
wherein:
the velocity and position of the ith particle for the t-th generation,
is the ith particle individual optimal particle and the global optimal particle of the t generation, w is the inertia constant amount 0.5, c1、c2Is two learning factors, r1、r2Is a random number between two (0, 1).
In the embodiment of the invention, the particles are in the on-off state set and only comprise two states of 0 and 1, so that the positions of the updated particles are only inverted.
Step S4034: and carrying out load flow calculation again according to the updated particle positions.
Step S4035: it is determined whether the updated particle position is better than the non-updated particle position.
If yes, go to step S4036; otherwise, step S4037 is performed.
Step S4036: and updating the optimal position of the individual particles.
Step S4037: maintaining the optimal position of the individual particles.
Step S4038: and writing the information of the individual particles into the corresponding positions of the new set.
Step S4039: it is determined whether all particles have been traversed.
If yes, go to step S40310; otherwise, step S4033 is performed.
Step S40310: and outputting the individual optimal particle swarm.
Step S404: and updating the global optimal particles.
Specifically, as shown in fig. 4, step S404 includes the following processes:
step S4041: combining the new particle population with the old particle population to form a particle population of 2N in size.
Step S4042: and acquiring the aggregation weight omega of each particle corresponding to the target optimization function.
ω＝ω0+ρ(1-ω0)。
Wherein: omega0Taking 0.5; rho obeys [0,1 ]]Uniformly distributed random numbers.
Step S4043: and calculating a global optimal particle swarm with the size of N, wherein the global optimal particle swarm with the size of N is obtained by minimizing the sum of the target optimization functions after weighting in the particle swarm with the size of 2N.
Step S4044: and outputting the global optimal particle swarm.
Step S405: and judging whether the maximum iteration number is reached.
If yes, go to step S406; otherwise, return to step S403 until the maximum number of iterations is reached.
Step S406: and outputting the particle swarm.
Through the step S40, the algorithm is applied to the scene that distributed energy and automobile charging stations are accessed into the urban distribution network, and the multi-target urban distribution network reconstruction problem which is considered to reduce the network loss and improve the power supply reliability is solved. The multi-objective particle swarm optimization algorithm based on decomposition and differential evolution can reduce the network loss of the urban power distribution network to optimize the operation cost, and meanwhile, the power supply reliability level of the urban power distribution network is integrally improved. Therefore, the method has remarkable social and economic benefits.
Step S50: and reconstructing the network of the urban distribution network according to the on-off of the switch of each load node in the urban distribution network corresponding to the solution of the weighted sum minimum multi-objective optimization function.
Wherein the weighted sum is obtained from the solution of the multi-objective optimization function and the weight of each objective optimization function. Specifically, the solution of each objective optimization function is multiplied by the corresponding weight to obtain a product, and then summed.
To sum up, the urban distribution network reconstruction method of the embodiment of the invention takes the annual power shortage, the average power failure frequency and the average power failure time of the system as the reliability optimization targets, respectively makes a relatively comprehensive evaluation on the reliability from three angles of the power failure electric quantity, the frequency and the time of the system, simultaneously selects the active loss as the network loss optimization target, reconstructs the selectable nodes in the urban distribution network based on the multivariate optimization mechanism and the multivariate coding mechanism of the multi-objective particle swarm optimization algorithm of decomposition and differential evolution in the urban distribution network reconstruction, optimizes the distributed energy and the electric vehicle charging station multinomial variables, ensures that the distribution network is radial, considers the problems of the special distributed energy generation and the electric vehicle charging of the urban distribution network on the premise of meeting the requirements of feeder heat capacity, landing and transformer capacity and the like, by changing the switching state of the line switch, the power supply way of a user is changed, so that the network loss and the power supply voltage quality of the urban power distribution network are ensured to be in the optimal distribution network operation mode, the network loss of the urban power distribution network containing a distributed power supply and an automobile charging station can be optimized, the operation cost is reduced, and the reliability of the urban power distribution network is improved; the method automatically generates a plurality of groups of recommended schemes, and operators can balance the requirements of economy and reliability according to actual needs and flexibly select appropriate schemes.
The embodiment of the invention also provides a system for reconstructing the urban power distribution network. As shown in fig. 5, the system includes:
the establishing module 501 is used for establishing a multi-objective optimization function with the minimum annual power shortage amount of the system, the average outage duration time of the system, the average outage frequency of the system and the minimum network loss of the urban power distribution network;
a determining module 502, configured to determine constraint conditions of the multi-objective optimization function;
and a setting module 503, configured to set on/off of a switch of each load node in the urban power distribution network.
And the solving module 504 is used for solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the on-off of the switch of each load node, the multi-objective optimization function and the constraint conditions.
And the reconstruction module 505 is configured to perform network reconstruction on the urban distribution network according to the on-off state of the switch of each load node in the urban distribution network corresponding to the solution of the weighted sum minimum multi-objective optimization function.
In summary, the urban distribution network reconfiguration system provided by the embodiment of the invention can optimize the network loss of the urban distribution network containing the distributed power supply and the automobile charging station, reduce the operation cost and improve the reliability of the urban distribution network; the method automatically generates a plurality of groups of recommended schemes, and operators can balance the requirements of economy and reliability according to actual needs and flexibly select appropriate schemes.
Specifically, by taking an IEEE69 node distribution network as an example, the system has 69 nodes, 74 lines, 5 tie switches, and a total load of 3802.2kW + j2694.6kvar, and the structure diagram of the network is shown in fig. 6. The method of the embodiments of the present invention was verified by the specific examples described above.
In an IEEE69 node test calculation example, the nodes 27, 30 and 39 are connected with a wind power station, and the rated capacity is 200 kW; the nodes 41, 48 and 56 are connected with a photovoltaic power station, and the rated capacity is 100 kW; the nodes 4, 50 and 68 are connected with an energy storage power station, and the rated capacity is 125 kW; the nodes 3, 19, 49 are connected to a vehicle charging station, and the maximum capacity is 100 kW. The switches that were open before reconfiguration were: 11-66, 13-20, 15-69, 27-54 and 39-48. After reconstruction by the optimization algorithm, the switches that are disconnected are determined as follows: 14-15, 44-45, 50-51, 11-66, 13-20, the weighted sum of the multi-objective optimization functions is minimal.
By adopting the method of the embodiment of the invention, the power distribution network after the optimal reconstruction scheme is selected, the network loss is reduced by 42.92%, and the voltage deviation index is improved by 53.21%. The patent shows only the voltage deviation index of one of the objective functions, as shown in fig. 7, for each node voltage before and after the network reconstruction in the 69-node distribution network. The reconstruction method improves the lowest node voltage of the 69-node distribution network, improves the voltage distribution of the whole network, and effectively improves the economy and the power supply reliability of the urban distribution network.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (5)
1. A method for reconstructing a network of an urban distribution network is characterized by comprising the following steps:
establishing a multi-objective optimization function with minimum annual power supply shortage amount, average system outage duration, average system outage frequency and network loss of the urban power distribution network;
determining constraint conditions of the multi-objective optimization functions and the weight of each objective optimization function;
the constraint conditions include: the method comprises the following steps of power distribution network topological structure constraint, node power balance constraint, line power constraint, distributed power supply power constraint, node voltage constraint and node current constraint;
the power distribution network topology constraints include: g belongs to G, wherein G is a network structure found through reconstruction, and G is a set of all topological structures meeting network constraint conditions;
the node power balancing constraints include:
and
wherein, PisAnd QisRespectively performing active injection and reactive injection on the node i; u shapeiIs the voltage amplitude of node i; j belongs to i and represents that the node j is connected with the node i; gijAnd BijRespectively a real part and an imaginary part of the node admittance matrix; thetaijIs the voltage phase angle difference between nodes i and j;
the line power constraints include: sl<SlmaxL is 1, 2, … …, L, wherein SlFor the actual transmission power of the line l, SlmaxIs the maximum capacity of the transmission of line l;
the distributed power supply power constraints include:
wherein S isDGo,maxIs the maximum apparent power, P, of the o-th distributed power supplyDGoActive power, Q, for the o-th distributed power supplyDGoThe reactive power of the o-th distributed power supply;
the node voltage constraints include: u shapeimin≤Ui≤UimaxI is 1, 2, … …, m, wherein UiminFor node i minimum allowed voltage, UimaxThe maximum allowable voltage of a node i is defined, and m is the number of nodes;
the node current constraints include: i isimin≤Ii≤IimaxI is 1, 2, … …, m, wherein, IiminMinimum allowable Current for node I, IimaxThe maximum allowable current of a node i is defined, and m is the number of nodes;wherein the nodes are all load nodes;
setting an initial on-off value of a switch of each load node in the urban power distribution network;
solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the initial on-off value of the switch of each load node, the multi-objective optimization function and the constraint condition;
the method comprises the following steps: inputting initial information of the urban distribution network, and setting the particle swarm scale; the initial information of the urban distribution network comprises parameters required for solving a multi-objective optimization function;
generating N inertia weight vectors according to the homogenization direction vector, and setting iteration times; the step is equivalent to setting the weight of each objective optimization function, namely, converting the multi-objective optimization problem of N objective optimization functions into the weighted summation problem of N single objective function optimization problems; updating the individual optimal particles; the method comprises the following steps:
inputting particle swarm information; carrying out load flow calculation; updating the particle speed and position;
carrying out load flow calculation again according to the updated particle positions;
judging whether the updated particle position is superior to the non-updated particle position;
if so, updating the optimal position of the individual particle; otherwise, keeping the optimal position of the individual particles;
writing the updated information of the optimal position of the individual particle into a corresponding position of a new set;
judging whether all the particles are traversed or not;
if yes, outputting an individual optimal particle swarm; otherwise, returning to the step of updating the particle speed and the particle position;
updating the global optimal particles; the method comprises the following steps:
combining the new particle swarm and the old particle swarm to form a particle swarm with the size of 2N;
acquiring the aggregation weight of the target optimization function corresponding to each particle; obtaining a global optimal particle swarm with the size of N, wherein the global optimal particle swarm with the size of N is obtained by minimizing the sum of the target optimization function after weighting in the particle swarm with the size of 2N;
outputting a global optimal particle swarm;
judging whether the maximum iteration times is reached;
if yes, outputting a particle swarm; otherwise, returning to the step of updating the individual optimal particles, the step of updating the global optimal particles and the step of judging whether the maximum iteration times is reached or not until the maximum iteration times is reached;
and reconstructing the urban distribution network according to the on-off of the switch of each load node in the urban distribution network corresponding to the solution of the weighted sum of the minimum multi-objective optimization functions.
2. The method of claim 1,
the target optimization function of the annual power supply shortage amount of the system is as follows:
the objective optimization function of the average outage duration of the system is as follows:
the objective optimization function of the average outage frequency of the system is as follows:
wherein i is the load node in the system, m is the sum of the load nodes in the system, and La(i)Is the average load of load node i, ΨiIs the annual average outage time, λ, of the load node iiIs the annual average outage frequency, S, of the load node iiIs the apparent power of the load node i.
3. The method of claim 1, wherein the objective optimization function for minimizing the network loss of the urban distribution network is:
wherein f is1For system loss, L is the number of system lines, klIs a switch state variable, 0 represents open, 1 represents closed, rlIs the line l resistance, PlAnd QlRespectively the active and reactive power, V, flowing through the ends of the line llIs the line end node voltage.
4. The method of claim 1, wherein:
when the distributed power supply is a PQ constant node, the model of load flow calculation is as follows:
wherein, P1sAnd Q1sThe active power and the reactive power of the PQ constant DG are respectively;
when the distributed power supply is a PV constant node, the model of the load flow calculation of the energy storage battery in the discharging state is as follows:
wherein, P2sAnd V2sRespectively the active power and voltage of the PV constant DG; the model of the load flow calculation of the electric vehicle charging and energy storage battery in the charging state is as follows:
wherein, P3sAnd V3sRespectively charging the electric automobile and storing the active power and the voltage of the energy storage battery in a charging state;
when the distributed power supply is a PI constant node, the model of the photovoltaic load flow calculation is as follows:
wherein, P4sAnd I4sRespectively the active power and the current of the PI constant DG.
5. A system for reconstructing a network of an urban power distribution network is characterized by comprising:
the system comprises an establishing module, a judging module and a control module, wherein the establishing module is used for establishing a multi-objective optimization function with the minimum annual power shortage amount of the system, the average outage duration time of the system, the average outage frequency of the system and the minimum network loss of the urban power distribution network;
the determining module is used for determining the constraint conditions of the multi-objective optimization functions and the weight of each objective optimization function; the constraint conditions include: the method comprises the following steps of power distribution network topological structure constraint, node power balance constraint, line power constraint, distributed power supply power constraint, node voltage constraint and node current constraint;
the power distribution network topology constraints include: g belongs to G, wherein G is a network structure found through reconstruction, and G is a set of all topological structures meeting network constraint conditions;
the node power balancing constraints include:
and
wherein, PisAnd QisRespectively performing active injection and reactive injection on the node i; u shapeiIs the voltage amplitude of node i; j belongs to i and represents that the node j is connected with the node i; gijAnd BijRespectively a real part and an imaginary part of the node admittance matrix; thetaijIs the voltage phase angle difference between nodes i and j;
the line i power constraints include: sl<SlmaxL is 1, 2, … …, L, wherein SlFor the actual transmission power of the line l, SimaxIs the maximum capacity of delivery for line i;
the distributed power supply power constraints include:
wherein S isDGo,maxIs the o-th distributed powerMaximum apparent power of the source, PDGoActive power, Q, for the o-th distributed power supplyDGoThe reactive power of the o-th distributed power supply;
the node voltage constraints include: u shapeimin≤Ui≤UimaxI is 1, 2, … …, m, wherein UiminFor node i minimum allowed voltage, UimaxThe maximum allowable voltage of a node i is defined, and m is the number of nodes;
the node current constraints include: i isimin≤Ii≤IimaxI is 1, 2, … …, m, wherein, IiminMinimum allowable Current for node I, IimaxThe maximum allowable current of a node i is defined, and m is the number of nodes; wherein the nodes are all load nodes;
the setting module is used for setting an initial on-off value of a switch of each load node in the urban power distribution network;
the solving module is used for solving the multi-objective optimization function by adopting a multi-objective particle swarm optimization algorithm based on decomposition and differential evolution according to the initial on-off value of the switch of each load node, the multi-objective optimization function and the constraint condition;
the method comprises the following steps: inputting initial information of the urban distribution network, and setting the particle swarm scale; the initial information of the urban distribution network comprises parameters required for solving a multi-objective optimization function;
generating N inertia weight vectors according to the homogenization direction vector, and setting iteration times; the step is equivalent to setting the weight of each objective optimization function, namely, converting the multi-objective optimization problem of N objective optimization functions into the weighted summation problem of N single objective function optimization problems; updating the individual optimal particles; the method comprises the following steps:
inputting particle swarm information; carrying out load flow calculation; updating the particle speed and position;
carrying out load flow calculation again according to the updated particle positions;
judging whether the updated particle position is superior to the non-updated particle position;
if so, updating the optimal position of the individual particle; otherwise, keeping the optimal position of the individual particles;
writing the updated information of the optimal position of the individual particle into a corresponding position of a new set;
judging whether all the particles are traversed or not;
if yes, outputting an individual optimal particle swarm; otherwise, returning to the step of updating the particle speed and the particle position;
updating the global optimal particles; the method comprises the following steps:
combining the new particle swarm and the old particle swarm to form a particle swarm with the size of 2N;
acquiring the aggregation weight of the target optimization function corresponding to each particle; obtaining a global optimal particle swarm with the size of N, wherein the global optimal particle swarm with the size of N is obtained by minimizing the sum of the target optimization function after weighting in the particle swarm with the size of 2N;
outputting a global optimal particle swarm;
judging whether the maximum iteration times is reached;
if yes, outputting a particle swarm; otherwise, returning to the step of updating the individual optimal particles, the step of updating the global optimal particles and the step of judging whether the maximum iteration times is reached or not until the maximum iteration times is reached;
and the reconstruction module is used for reconstructing the urban distribution network according to the on-off state of the switch of each load node in the urban distribution network corresponding to the solution of the multi-objective optimization function with the minimum weighted sum.
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