CN112332431A - Power distribution network reconstruction method considering electric automobile participating in frequency modulation - Google Patents

Power distribution network reconstruction method considering electric automobile participating in frequency modulation Download PDF

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CN112332431A
CN112332431A CN202011340235.7A CN202011340235A CN112332431A CN 112332431 A CN112332431 A CN 112332431A CN 202011340235 A CN202011340235 A CN 202011340235A CN 112332431 A CN112332431 A CN 112332431A
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frequency modulation
distribution network
power distribution
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electric automobile
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CN112332431B (en
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李中伟
张杨柳
程丽
金显吉
白子扬
佟为明
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Harbin Institute of Technology
Harbin Engineering University
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Harbin Engineering University
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/003—Load forecast, e.g. methods or systems for forecasting future load demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48—Controlling the sharing of the in-phase component
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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Abstract

The invention discloses a power distribution network reconstruction method considering electric automobile participation frequency modulation, which is a method for performing optimization reconstruction on a power distribution network when an electric automobile participates in power system frequency modulation by taking electric automobile load prediction as a basis, taking the lowest distribution line loss and voltage offset as optimization targets and taking the no heavy load of a power distribution network line and the limit frequency modulation power which can be provided by the electric automobile at the current node as optimization conditions. The method solves the problem that the algorithm is difficult to converge due to the fact that the solution space dimension is increased when the structure of the power distribution network is complex, and optimization calculation is carried out by combining the electric automobile load participation frequency modulation process with the power distribution network reconstruction.

Description

Power distribution network reconstruction method considering electric automobile participating in frequency modulation
Technical Field
The invention relates to a power distribution network optimization reconstruction method in load side frequency modulation, in particular to a power distribution network reconstruction method considering that an electric vehicle participates in frequency modulation.
Background
Since the 70 s in the 20 th century, the reconstruction of the distribution network is only aimed at achieving the purpose of improving the voltage quality of the distribution network by reducing the active network loss of the distribution network under various conditions and balancing the load distribution of the distribution network without great change.
In the case of switches in a power distribution grid system, the switches can be roughly divided into two categories, one with a larger number of sectionalizing switches and one with a smaller number of tie switches. At the beginning of the design, the distribution network can form closed circuit through section switch and interconnection switch, and in the operation process, through section switch and interconnection switch's cooperation, realize the open loop operation. Under the condition of meeting the power flow constraint and the node constraint, the two types of switches are controlled, the topological structure and the power flow distribution of the power distribution network are changed, and the purpose of reconstructing the power distribution network is achieved. With the access of a large number of Distributed power Sources (DGs) and various flexible loads, reconfiguration of the distribution network to combine DGs with flexible loads begins to occur. For the power grid in an island operation mode, the position and the capacity of the DG connected into the power distribution network can be determined based on the mode that the DG supports the continuous operation of the power distribution network, and then the corresponding power distribution network frame structure is analyzed according to different power distribution network fault types. However, in the current algorithm, the two parts are not combined tightly, and the optimal position and capacity of the DG accessed to the power distribution network and the respectively optimal solution of the grid structure of the power distribution network in a fault state are not unified optimal solutions of the problem. And the DG switch state and the output power are combined with the switch state of the power distribution network and simultaneously used as input parameters, and the DG is regarded as a schedulable part in the power distribution network, so that a corresponding solution with the optimal voltage quality of the power distribution network can be obtained. The reconstruction analysis of the power distribution network improves the operation efficiency of the power distribution network in a normal state and a fault state.
Because the essential reason of the occurrence of the frequency event of the power system is that the active output is not matched with the load active demand, when the frequency event occurs, the power system may have the problems that the voltage of part of nodes is too low, and part of lines are overloaded or overloaded. When the electric automobile load participates in secondary frequency modulation, no matter the charging power is reduced or active power is provided for the power grid, the influence is generated on the power flow of the power distribution network, therefore, when the frequency modulation capacity provided by the electric automobile load is larger than the capacity required by the system, the electric automobiles located at the nodes of the power distribution network are selected and the frequency modulation output of the electric automobiles is provided, and the influence is generated on the optimal operation architecture of the power distribution network. Therefore, if the power distribution network architecture can be optimized by combining the output condition of the electric automobile when a frequency event occurs, the active loss of the power distribution network can be further reduced, and the frequency quality and the voltage quality of the power distribution network can be improved.
Disclosure of Invention
When the load of the electric automobile participates in secondary frequency modulation, the load influences the power flow of the power distribution network, so that the power distribution network framework is optimized by combining the output condition of the electric automobile when a frequency event occurs, and the power distribution network reconstruction method considering the participation of the electric automobile in frequency modulation is provided. The method is based on electric vehicle load prediction, the lowest distribution line loss and voltage offset is taken as an optimization target, no heavy load occurs on a distribution network line and the limit frequency modulation power which can be provided by an electric vehicle at the current node is taken as an optimization condition, and the method for optimizing and reconstructing the distribution network is carried out when the electric vehicle participates in the frequency modulation of a power system, so that the active loss of the distribution network is further reduced, and the frequency quality and the voltage quality of the distribution network are improved.
The purpose of the invention is realized by the following technical scheme:
a power distribution network reconstruction method considering that an electric vehicle participates in frequency modulation comprises the following steps:
step one, optimizing a grid structure of a power distribution network:
firstly, initializing a node parameter, a branch parameter and a ring network parameter of a power distribution network according to a topology structure of the power distribution network, generating a node branch matrix, and numbering switches;
secondly, coding the switch reconstructed by the power distribution network by adopting an integer coding mode; determining the dimension of a solution space as the number H of system loops by combining a topological structure of the power distribution network, and setting the upper limit, the lower limit, the population scale and the maximum iteration number of each one-dimensional parameter;
thirdly, constructing a node branch incidence matrix based on the original parameters of the power distribution network;
fourthly, establishing a node adjacency matrix through the node branch incidence matrix;
fifthly, calculating the electrical distance from the power supply node to each node by using a Floyd algorithm, wherein the distance between the nodes is the branch impedance;
sixthly, judging whether the electrical distance matrix parameters have infinite values, if the infinite values exist, proving that the nodes are not communicated with the power supply points, reconstructing the power distribution network based on the generated solution to form isolated islands or isolated points, wherein the solution is not feasible, and skipping to the third step; if not, jumping to the seventh step;
seventhly, judging the size relation between the number of distance parameters which are not zero and are not infinite parameters in the adjacent matrix and 2N-2, if the number of the distance parameters is more than 2N-2, indicating that a loop exists in the power distribution network formed by the solution, the solution is not feasible, returning the calculated value of the fitness function at the moment, and jumping to the third step; if the number of the power distribution network loss is equal to 2N-2, the system has no loop, and the solution is judged to be a feasible solution for power distribution network reconstruction, wherein the fitness function is the power distribution network loss
Figure BDA0002798400530000031
Wherein F is total loss of the distribution network, b is total branch number of the distribution network, and kjTo correspond to the switching state of the branch, rjIs a branch resistance, PijTo flow through the branchActive power of, QijFor reactive power flowing through the branch, UjIs the voltage of the branch end node;
step two, electric automobile frequency modulation capacity distribution and allocation optimization:
the method comprises the steps of firstly, substituting feasible solutions into a grid structure, inputting the grid structure as a network system of the electric automobile load participation system frequency modulation, inputting the electric automobile participation frequency modulation load limit value of each node, and counting N according to the required frequency modulation nodesevThe dimension of the corresponding solution space is 2NevSetting the upper and lower limits of each dimension, the population scale and the maximum iteration number of the solution of the electric automobile load participation system frequency modulation;
secondly, optimizing the distribution and distribution of the capacity of the electric vehicle load participating in frequency modulation according to an inner-layer quantum particle swarm algorithm, and calculating the fitness of corresponding distribution and distribution by applying a fitness function to obtain an individual optimal value pbestAnd global optimum gbest；
Thirdly, iteratively updating the population according to a target function of the optimal scheduling of the electric vehicle load participating in the frequency modulation capacity by using a particle swarm algorithm, and obtaining the optimal distribution and distribution of the electric vehicle load participating in the frequency modulation capacity under the grid structure when the target function reaches the minimum value, wherein the target function is the minimum total loss of the power distribution network after reconstruction
Figure BDA0002798400530000041
Fourthly, the optimized distribution and distribution of the load participation frequency modulation capacity of the electric automobile obtained in the third step are used as the distribution and distribution data of the load participation frequency modulation capacity of the electric automobile matched with the grid structure;
fifthly, calculating and updating the adaptive value of feasible solution particles for the distribution and distribution of the load participation frequency modulation capacity of the electric automobile according to the objective function of the outer grid structure to obtain the corresponding individual optimal value pbestAnd global optimum gbestAt this time, for the outer layer algorithm, only the node power parameter is changed, and the other parameters are not changed;
judging whether the set maximum iteration times are reached or whether the particles obtain a better value within the set maximum iteration number or not, and if the set maximum iteration times are reached or the particles do not obtain a better value within the set maximum iteration number, jumping out of a loop and outputting a reconstruction result and a corresponding electric vehicle load to participate in distribution and distribution of the frequency modulation capacity; otherwise, continuing iteration, updating the network structure by using the obtained new solution, and jumping to the sixth step of the first step.
Compared with the prior art, the invention has the following advantages:
the method comprises the steps that an integral particle swarm algorithm is adopted on the outer layer to optimize a grid structure of a power distribution network, a quantum particle swarm algorithm is applied to the inner layer, distribution and distribution of frequency modulation capacity of the electric vehicle are optimized on the basis of an optimization result of the outer layer, an individual optimal solution and an integral optimal solution of the inner layer are reserved as feasible solutions of corresponding grid structures, and through calculation of corresponding fitness functions, combination of the optimal grid structure and distribution of the frequency modulation capacity of the electric vehicle is obtained under the same iteration through the method. The method solves the problem that the algorithm is difficult to converge due to the fact that the solution space dimension is increased when the structure of the power distribution network is complex, and optimization calculation is carried out by combining the electric automobile load participation frequency modulation process with the power distribution network reconstruction.
Drawings
FIG. 1 is a flow chart of a method for determining feasibility of a solution of a power distribution network reconstruction algorithm;
FIG. 2 is a power distribution network reconfiguration strategy flow chart considering that an electric vehicle participates in frequency modulation;
FIG. 3 is a system architecture of an IEEE33 node distribution network;
FIG. 4 is an optimized distribution network structure
FIG. 5 is a comparison of voltage amplitudes of nodes before and after frequency modulation;
FIG. 6 is a comparison of total network loss before and after optimization;
fig. 7 is a graph comparing the total loss of different strategies.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings, but not limited thereto, and any modification or equivalent replacement of the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention shall be covered by the protection scope of the present invention.
The invention provides a power distribution network reconstruction method considering electric automobile participation frequency modulation, which takes the lowest distribution line loss and voltage offset as optimization targets, takes the no overloading of a power distribution network line and the limit frequency modulation power which can be provided by an electric automobile at the current node as optimization conditions, solves the power distribution network reconstruction considering electric automobile participation frequency modulation through a double-layer optimization model, optimizes a power distribution network grid structure by adopting an integer particle swarm algorithm at the outer layer, applies a quantum particle swarm algorithm at the inner layer, optimizes the distribution and distribution of the frequency modulation capacity of the electric automobile on the basis of the optimization result of the outer layer, reserves the individual optimal solution and the overall optimal solution of the inner layer as feasible solutions of the corresponding grid structure, calculates and updates the distribution of the load participation frequency modulation capacity of the electric automobile and the adaptive value of the distributed feasible solution particles according to the objective function of the grid structure of the outer layer, obtain corresponding individual optimal value pbestAnd global optimum gbestAnd obtaining the combination of the optimal grid structure and the frequency modulation capacity distribution and distribution of the electric automobile under the same iteration. As shown in fig. 1 and 2, the specific implementation steps are as follows:
step one, optimizing a grid structure of a power distribution network:
step one, optimizing a grid structure of a power distribution network:
firstly, initializing a node parameter, a branch parameter and a ring network parameter of a power distribution network according to a topology structure of the power distribution network, generating a node branch matrix, and numbering switches;
secondly, coding the switch reconstructed by the power distribution network by adopting an integer coding mode; determining the dimension of a solution space as the number H of system loops by combining a topological structure of the power distribution network, and setting the upper limit, the lower limit, the population scale and the maximum iteration number of each one-dimensional parameter;
thirdly, constructing a node branch incidence matrix based on the original parameters of the power distribution network;
fourthly, establishing a node adjacency matrix through the node branch incidence matrix;
fifthly, calculating the electrical distance from the power supply node to each node by using a Floyd algorithm, wherein the distance between the nodes is the branch impedance;
sixthly, judging whether the electrical distance matrix parameters have infinite values, if the infinite values exist, proving that the nodes are not communicated with the power supply points, reconstructing the power distribution network based on the generated solution to form isolated islands or isolated points, wherein the solution is not feasible, and skipping to the third step; if not, jumping to the seventh step;
seventhly, judging the size relation between the number of distance parameters which are not zero and are not infinite parameters in the adjacent matrix and 2N-2, if the number of the distance parameters is more than 2N-2, indicating that a loop exists in the power distribution network formed by the solution, the solution is not feasible, returning the calculated value of the fitness function at the moment, and jumping to the third step; if the number of the power distribution network reconstruction solutions is equal to 2N-2, the system has no loop, and the solution is judged to be a feasible solution for power distribution network reconstruction.
Regarding the initial solution of the power distribution network reconstruction, integer type coding is adopted, and if the number of nodes of the power distribution network is N, the number of reconstructed front links of the power distribution network is N-1, and the number of interconnection switches is B. The N-1 branches before the power distribution network is reconstructed are numbered, the switching states of the B interconnection switches are used as the switching states of the Nth to (N + B-1) th branches, and the switching states are mixed with the original branches to generate a power distribution network frame topology with all switches closed. When generating the corresponding solution, assuming that all the switches are closed, at this time, the distribution network will generate B loops, and then B switches are selected to be opened, thereby forming an initial solution for reconstruction of the distribution network. The B switches which are disconnected are used as B dimensions of the solution, and the upper and lower limit parameters of each dimension are influenced by the topological structure of the power distribution network.
Power distribution networks are also subject to the following constraints:
(1) and (3) power flow constraint:
Figure BDA0002798400530000081
Figure BDA0002798400530000082
wherein N is the number of nodes, ViVoltage corresponding to node i, GijConductance for branch ij, BijSusceptance, θ, of branch ijijThe node voltage phase difference between node i and node j.
(2) Voltage and current restraint:
Uimin≤Ui≤Uimax；
Ii＜Iimax；
in the formula of UiIs the voltage of node i, UimaxIs the upper voltage limit of node i, UimaxIs the lower voltage limit of node I, IiTo flow through branch biCurrent of (I)imaxIs a branch biMaximum allowable current value, NbIs a distribution network branch matrix.
(3) Constraint of transmission capacity of distribution line:
Pj 2+Qj 2≤0.8·Sjmax；
in the formula, PjActive power, Q, flowing for the j branchjFor the reactive power, S, flowing through the jth branchjmaxIs the rated capacity of the line.
(4) Electric automobile load frequency modulation standby restraint:
g∈Gcon；
Figure BDA0002798400530000083
in the formula, g is the current grid structure of the power distribution network; gconThe method is a loop-free power distribution network architecture; gislThe method is an island-free power distribution network architecture.
Step two, electric automobile frequency modulation capacity distribution and allocation optimization:
the method comprises the steps of firstly, substituting feasible solutions into a grid structure, inputting the grid structure as a network system of the electric automobile load participation system frequency modulation, inputting the electric automobile participation frequency modulation load limit value of each node, and counting N according to the required frequency modulation nodesevTo obtain a correspondingHas a dimension of 2NevSetting the upper and lower limits of each dimension, the population scale and the maximum iteration number of the solution of the electric automobile load participation system frequency modulation;
secondly, optimizing the distribution and distribution of the capacity of the electric vehicle load participating in frequency modulation according to an inner-layer quantum particle swarm algorithm, and calculating the fitness of corresponding distribution and distribution by applying a fitness function to obtain an individual optimal value pbestAnd global optimum gbest；
Thirdly, iteratively updating the population according to a target function of the optimal scheduling of the electric vehicle load participating in the frequency modulation capacity by using a particle swarm algorithm, and obtaining the optimal distribution and allocation of the electric vehicle load participating in the frequency modulation capacity under the grid structure when the target function reaches the minimum value;
fourthly, the optimized distribution and distribution of the load participation frequency modulation capacity of the electric automobile obtained in the third step are used as the distribution and distribution data of the load participation frequency modulation capacity of the electric automobile matched with the grid structure;
fifthly, calculating and updating the adaptive value of feasible solution particles for the distribution and distribution of the load participation frequency modulation capacity of the electric automobile according to the objective function of the outer grid structure to obtain the corresponding individual optimal value pbestAnd global optimum gbestAt this time, for the outer layer algorithm, only the node power parameter is changed, and the other parameters are not changed;
judging whether the set maximum iteration times are reached or whether the particles obtain a better value within the set maximum iteration number or not, and if the set maximum iteration times are reached or the particles do not obtain a better value within the set maximum iteration number, jumping out of a loop and outputting a reconstruction result and a corresponding electric vehicle load to participate in distribution and distribution of the frequency modulation capacity; otherwise, continuing iteration, updating the network structure by using the obtained new solution, and jumping to the sixth step of the first step.
The distribution and allocation of the electric vehicle participating in the frequency modulation capacity is also constrained by the following:
(1) the electric automobile load participates in the restraint of the frequency modulation capacity:
Figure BDA0002798400530000101
in the formula, NevThe number of nodes participating in frequency modulation for the electric automobile load; pnmevThe frequency modulation capacity is provided for the electric automobile load at the corresponding node; pzrlThe system needs the total capacity of frequency modulation provided by the electric automobile load.
(2) Electric automobile load frequency modulation standby restraint:
PV2G≤PV2Gmax；
in the formula, PV2GFrequency modulation standby is provided for electric automobile loads; pV2GmaxAnd the spare upper limit for load frequency modulation of the electric automobile is provided.
Example (b):
the test system is shown in the IEEE33 node power distribution system of fig. 3. The power distribution system has 32 nodes, wherein the power source node is 1, the number is 0, the load node is 31, the T node is 3, and the end node is 4. The line voltage reference value is 12.66kV, and the capacity reference value is 10 MVA. Table 1 shows the load power of each node, and table 2 shows the branch data.
TABLE 1 IEEE33 nodal distribution network power distribution
Figure BDA0002798400530000102
Figure BDA0002798400530000111
Table 2 IEEE33 node distribution network line data
Figure BDA0002798400530000112
The specific parameters of the power distribution network reconfiguration strategy provided by the embodiment are set as follows:
(1) for grid structure optimization, the solution space dimension is 5, and the upper and lower limits of each dimension are [1,37 ]]Convergence ofAccuracy set to 10-5The population number is 80, the maximum iteration number is 350, and the boundary variation rate is 0.05.
(2) For the optimization of the capacity of the electric automobile participating in frequency modulation, the solution space dimension is 10, wherein the upper and lower limits of the node dimension are [1,32 ]]The volume dimension is [1,30 ]]Convergence accuracy is set to 10-5 Population number 160, maximum number of iterations 350.
Assuming that the frequency modulation task required to be carried by the electric vehicle load is 5 load nodes, the capacity needs to be reduced by 100 kW.
In order to compare the effects before and after optimization, 5 nodes are randomly selected, the frequency modulation capacity of each node is randomly selected, and the comparison is performed with the optimal node position and the frequency modulation capacity obtained by optimizing the scheduling strategy, wherein the randomly selected node position and the frequency modulation capacity, and the optimal node position and the frequency modulation capacity are shown in table 3. The optimized power distribution network frame structure is shown in fig. 4.
TABLE 3 IEEE33 node distribution network node position and capacity for participating in system frequency modulation
Figure BDA0002798400530000121
Fig. 5 is a comparison of node voltage amplitudes after IEEE33 node power distribution network system reconstruction, and it can be seen from fig. 5 that voltage values of nodes of a distribution line are increased after electric vehicle load participates in system frequency modulation, and a difference between a voltage of a terminal node of the line and a power supply node voltage is reduced.
Fig. 6 is a comparison graph of the total network loss of the power distribution network before and after frequency modulation, and it can be seen from fig. 6 that after the power distribution network with the electric vehicle load participating in frequency modulation is reconstructed, the total network loss of the system is reduced from 203.6kW to 144.4 kW. The system loss can be effectively reduced if the electric automobile load can be used for participating in system frequency modulation.
Fig. 7 is a network loss comparison graph of a power distribution network reconstruction strategy of randomly selected load nodes of the electric vehicle participating in system frequency modulation and a power distribution network reconstruction strategy taking account of the participation of the electric vehicle in frequency modulation, which is provided by the invention, and the network loss after the power distribution network of the randomly selected load nodes is reconstructed is 194.4 kW. The network loss after the electric automobile participates in the frequency modulation power distribution network reconstruction is 144.4kW, and it can be seen that the power distribution network reconstruction strategy considering the electric automobile participates in the frequency modulation can reduce the network loss of the power distribution network, reduce the network loss rate, improve the voltage quality of the system, reduce the operation cost of the system and be beneficial to the safety and the stability of the system.

Claims (5)

1. A power distribution network reconstruction method considering that an electric vehicle participates in frequency modulation is characterized by comprising the following steps:
step one, optimizing a grid structure of a power distribution network:
firstly, initializing a node parameter, a branch parameter and a ring network parameter of a power distribution network according to a topology structure of the power distribution network, generating a node branch matrix, and numbering switches;
secondly, coding the switch reconstructed by the power distribution network by adopting an integer coding mode; determining the dimension of a solution space as the number H of system loops by combining a topological structure of the power distribution network, and setting the upper limit, the lower limit, the population scale and the maximum iteration number of each one-dimensional parameter;
thirdly, constructing a node branch incidence matrix based on the original parameters of the power distribution network;
fourthly, establishing a node adjacency matrix through the node branch incidence matrix;
fifthly, calculating the electrical distance from the power supply node to each node by using a Floyd algorithm, wherein the distance between the nodes is the branch impedance;
sixthly, judging whether the electrical distance matrix parameters have infinite values, if the infinite values exist, proving that the nodes are not communicated with the power supply points, reconstructing the power distribution network based on the generated solution to form isolated islands or isolated points, wherein the solution is not feasible, and skipping to the third step; if not, jumping to the seventh step;
seventhly, judging the size relation between the number of distance parameters which are not zero and are not infinite parameters in the adjacent matrix and 2N-2, if the number of the distance parameters is more than 2N-2, indicating that a loop exists in the power distribution network formed by the solution, the solution is not feasible, returning the calculated value of the fitness function at the moment, and jumping to the third step; if the number of the power distribution network reconstruction solutions is equal to 2N-2, the system has no loop, and the solution is judged to be a feasible solution for power distribution network reconstruction;
step two, electric automobile frequency modulation capacity distribution and allocation optimization:
the method comprises the steps of firstly, substituting feasible solutions into a grid structure, inputting the grid structure as a network system of the electric automobile load participation system frequency modulation, inputting the electric automobile participation frequency modulation load limit value of each node, and counting N according to the required frequency modulation nodesevThe dimension of the corresponding solution space is 2NevSetting the upper and lower limits of each dimension, the population scale and the maximum iteration number of the solution of the electric automobile load participation system frequency modulation;
secondly, optimizing the distribution and distribution of the capacity of the electric vehicle load participating in frequency modulation according to an inner-layer quantum particle swarm algorithm, and calculating the fitness of corresponding distribution and distribution by applying a fitness function to obtain an individual optimal value pbestAnd global optimum gbest；
Thirdly, iteratively updating the population according to a target function of the optimal scheduling of the electric vehicle load participating in the frequency modulation capacity by using a particle swarm algorithm, and obtaining the optimal distribution and allocation of the electric vehicle load participating in the frequency modulation capacity under the grid structure when the target function reaches the minimum value;
fourthly, the optimized distribution and distribution of the load participation frequency modulation capacity of the electric automobile obtained in the third step are used as the distribution and distribution data of the load participation frequency modulation capacity of the electric automobile matched with the grid structure;
fifthly, calculating and updating the adaptive value of feasible solution particles for the distribution and distribution of the load participation frequency modulation capacity of the electric automobile according to the objective function of the outer grid structure to obtain the corresponding individual optimal value pbestAnd global optimum gbestAt this time, for the outer layer algorithm, only the node power parameter is changed, and the other parameters are not changed;
judging whether the set maximum iteration times are reached or whether the particles obtain a better value within the set maximum iteration number or not, and if the set maximum iteration times are reached or the particles do not obtain a better value within the set maximum iteration number, jumping out of a loop and outputting a reconstruction result and a corresponding electric vehicle load to participate in distribution and distribution of the frequency modulation capacity; otherwise, continuing iteration, updating the network structure by using the obtained new solution, and jumping to the sixth step of the first step.
2. The method for reconstructing the power distribution network by taking the electric vehicle into consideration for frequency modulation according to claim 1, wherein the fitness function is the network loss of the power distribution network, and the calculation formula is as follows:
Figure FDA0002798400520000031
wherein F is total loss of the distribution network, b is total branch number of the distribution network, and kjTo correspond to the switching state of the branch, rjIs a branch resistance, PijFor active power flowing through the branch, QijFor reactive power flowing through the branch, UjIs the voltage at the end node of the branch.
3. The method of claim 1, wherein the power distribution network is constrained by the following constraints:
(1) and (3) power flow constraint:
Figure FDA0002798400520000032
Figure FDA0002798400520000033
wherein N is the number of nodes, ViVoltage corresponding to node i, GijConductance for branch ij, BijSusceptance, θ, of branch ijijThe node voltage phase difference of the node i and the node j is obtained;
(2) voltage and current restraint:
Uimin≤Ui≤Uimax；
Ii＜Iimax；
in the formula of UiIs the voltage of node i, UimaxIs the upper voltage limit of node i, UimaxIs the lower voltage limit of node I, IiTo flow through branch biCurrent of (I)imaxIs a branch biMaximum allowable current value, NbA distribution network branch matrix is formed;
(3) constraint of transmission capacity of distribution line:
Pj 2+Qj 2≤0.8·Sjmax；
in the formula, PjActive power, Q, flowing for the j branchjFor the reactive power, S, flowing through the jth branchjmaxIs the rated capacity of the line;
(4) electric automobile load frequency modulation standby restraint:
g∈Gcon；
Figure FDA0002798400520000041
in the formula, g is the current grid structure of the power distribution network; gconThe method is a loop-free power distribution network architecture; gislThe method is an island-free power distribution network architecture.
4. The power distribution network reconstruction method considering the participation of the electric automobile in frequency modulation according to claim 1, wherein the objective function is that the total network loss of the power distribution network after reconstruction is minimum, and the calculation formula is as follows:
Figure FDA0002798400520000042
wherein F is total loss of the distribution network, b is total branch number of the distribution network, and kjTo correspond to the switching state of the branch, rjIs a branch resistance, PijFor active power flowing through the branch, QijFor reactive power flowing through the branch, UjIs the end node of the branchThe voltage of (c).
5. The method of claim 1, wherein the distribution and allocation of the capacity of the electric vehicle participating in the frequency modulation are constrained as follows:
(1) the electric automobile load participates in the restraint of the frequency modulation capacity:
Figure FDA0002798400520000051
in the formula, NevThe number of nodes participating in frequency modulation for the electric automobile load; pnmevThe frequency modulation capacity is provided for the electric automobile load at the corresponding node; pzrlThe system needs the total capacity of frequency modulation provided by the electric automobile load;
(2) electric automobile load frequency modulation standby restraint:
PV2G≤PV2Gmax；
in the formula, PV2GFrequency modulation standby is provided for electric automobile loads; pV2GmaxAnd the spare upper limit for load frequency modulation of the electric automobile is provided.
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