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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distribution network
node
branch
electric vehicle
frequency regulation
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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 Engineering University
Harbin Institute of Technology Shenzhen
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Harbin Institute of Technology Shenzhen
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    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/003Load forecast, e.g. methods or systems for forecasting future load demand
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/04Circuit arrangements for AC mains or AC distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/12Circuit arrangements for AC mains or AC distribution networks for adjusting voltage in AC networks by changing a characteristic of the network load
    • H02J3/14Circuit 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
    • H02J3/144Demand-response operation of the power transmission or distribution network
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/24Arrangements for preventing or reducing oscillations of power in networks
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00Circuit arrangements for AC mains or AC distribution networks
    • H02J3/38Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/48Controlling the sharing of the in-phase component
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10Power 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
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • HELECTRICITY
    • H02GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02JCIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/40Systems 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
    • YGENERAL 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
    • Y04INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04SSYSTEMS 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
    • Y04S20/00Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20End-user application control systems
    • Y04S20/222Demand 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)

1.一种计及电动汽车参与调频的配电网重构方法,其特征在于所述方法包括如下步骤:1. a distribution network reconfiguration method taking into account that electric vehicles participate in frequency regulation, it is characterized in that described method comprises the steps: 步骤一、配电网网架结构优化:Step 1. Optimization of distribution network grid structure: 第一步、根据配电网拓扑结构,初始化配电网节点参数、支路参数及环网参数,生成节点支路矩阵,对开关进行编号;The first step is to initialize the node parameters, branch parameters and ring network parameters of the distribution network according to the topology structure of the distribution network, generate a node branch matrix, and number the switches; 第二步、采用整数型编码方式对配电网重构的开关进行编码;结合配电网拓扑结构,确定解空间的维数为系统环路数H,设置每一维参数的上下限、种群规模及最大迭代次数;The second step is to use integer coding to encode the switches reconstructed in the distribution network. Combined with the topology of the distribution network, determine the dimension of the solution space as the number of loops in the system, and set the upper and lower limits and populations of each dimension parameter. size and maximum number of iterations; 第三步、基于配电网原始参数,构建节点支路关联矩阵;The third step is to construct the node branch association matrix based on the original parameters of the distribution network; 第四步、通过节点支路关联矩阵,建立节点邻接矩阵;The fourth step is to establish a node adjacency matrix through the node branch association matrix; 第五步、采用Floyd算法,计算电源节点到各个节点的电气距离,其中节点间距离为支路阻抗;The fifth step is to use the Floyd algorithm to calculate the electrical distance from the power supply node to each node, where the distance between nodes is the branch impedance; 第六步、判断电气距离矩阵参数是否存在无穷大值,若其中存在无穷大值,则证明存在节点与电源点不连通,基于该生成解的配电网重构存在孤岛或孤点,该解不可行,跳转第三步;若不存在,跳转第七步;The sixth step is to judge whether there is an infinite value in the parameters of the electrical distance matrix. If there is an infinite value, it proves that the node is not connected to the power point. The distribution network reconstruction based on the generated solution has islands or isolated points, and the solution is infeasible. , jump to the third step; if it does not exist, jump to the seventh step; 第七步、判断邻接矩阵中满足不为零且不为无穷大的参数的距离参量的个数与2N-2的大小关系,若其个数大于2N-2说明该解形成的配电网有环路,该解不可行,返回此时适应度函数的计算值,并跳转第三步;若其个数等于2N-2,则系统无环路,判断该解为配电网重构的可行解;Step 7: Determine the relationship between the number of distance parameters that satisfy the parameters that are not zero and not infinity and the size of 2N-2 in the adjacency matrix. If the number is greater than 2N-2, it means that the distribution network formed by the solution has loops If the number is equal to 2N-2, there is no loop in the system, and the solution is judged to be feasible for distribution network reconstruction. untie; 步骤二、电动汽车调频容量分布与分配优化:Step 2. Electric vehicle frequency regulation capacity distribution and allocation optimization: 第一步、将可行解代入网架结构,以该网架结构为电动汽车负荷参与系统调频的网络系统,输入各节点电动汽车参与调频负荷限值,根据所需调频节点数Nev,得到相应的解空间的维数为2Nev,设置电动汽车负荷参与系统调频的解各维上下限、种群规模及最大迭代次数;The first step is to substitute the feasible solution into the grid structure, and take the grid structure as the network system for the electric vehicle load to participate in the frequency regulation of the system, input the limit value of the electric vehicle to participate in the frequency regulation of each node, and obtain the corresponding frequency regulation according to the required number of frequency regulation nodes N ev . The dimension of the solution space is 2N ev , and the upper and lower limits of each dimension, the population size and the maximum number of iterations of the solution of the electric vehicle load participating in the system frequency modulation are set; 第二步、根据内层量子粒子群算法对电动汽车负荷参与调频容量的分布与分配进行优化,应用适应度函数计算相应分布与分配的适应度,得到个体最优值pbest和全局最优值gbestThe second step is to optimize the distribution and distribution of the electric vehicle load participating in the frequency regulation capacity according to the inner quantum particle swarm algorithm, and apply the fitness function to calculate the fitness of the corresponding distribution and distribution to obtain the individual optimal value p best and the global optimal value g best ; 第三步、运用粒子群算法按电动汽车负荷参与调频容量的优化调度的目标函数对种群进行迭代更新,当目标函数达到最小值时,即得到该网架结构下最优的电动汽车负荷参与调频容量的分布与分配;The third step is to use the particle swarm algorithm to iteratively update the population according to the objective function of the electric vehicle load participating in the optimal scheduling of the frequency regulation capacity. When the objective function reaches the minimum value, the optimal electric vehicle load participating in the frequency regulation under the grid structure is obtained. Distribution and allocation of capacity; 第四步、将第三步所得到的优化后的电动汽车负荷参与调频容量的分布与分配作为匹配该网架结构的电动汽车负荷参与调频容量的分布与分配数据;The fourth step is to use the optimized electric vehicle load participation frequency regulation capacity distribution and allocation obtained in the third step as the distribution and allocation data of the electric vehicle load participation frequency regulation capacity matching the grid structure; 第五步、根据外层的网架结构的目标函数计算更新电动汽车负荷参与调频容量的分布与分配的可行解粒子的适应值,得到相应的个体最优值pbest与全局最优值gbest,此时对于外层算法而言只改变了节点功率参数,其余参数并未发生改变;The fifth step is to calculate and update the fitness value of the feasible solution particles for the distribution and allocation of the electric vehicle load participating in the frequency regulation capacity according to the objective function of the outer grid structure, and obtain the corresponding individual optimal value p best and global optimal value g best , at this time, only the node power parameter is changed for the outer algorithm, and the other parameters are not changed; 第六步、判断是否达到了所设置的最大迭代次数或粒子在所设置的最大迭代数内是否得到了更好的值,若达到了最大迭代次数或粒子在所设置的最大迭代数内未得到更好的值,则跳出循环并输出重构结果与相应的电动汽车负荷参与调频容量的分布与分配;否则继续进行迭代,利用得到的新解对网络结构进行更新,并跳转至步骤一的第六步。The sixth step is to judge whether the set maximum number of iterations has been reached or whether the particle has obtained a better value within the set maximum number of iterations. If the maximum number of iterations has been reached or the particle has not been obtained within the set maximum number of iterations If the value is better, jump out of the loop and output the reconstruction result and the corresponding electric vehicle load to participate in the distribution and allocation of FM capacity; otherwise, continue to iterate, use the new solution obtained to update the network structure, and jump to step 1. Step 6. 2.根据权利要求1所述的计及电动汽车参与调频的配电网重构方法,其特征在于所述适应度函数为配电网网损,计算公式为:2. the distribution network reconfiguration method according to claim 1 taking into account that electric vehicles participate in frequency regulation, it is characterized in that described fitness function is distribution network network loss, and calculation formula is:
Figure FDA0002798400520000031
Figure FDA0002798400520000031
式中,F为配电网总网损,b为配电网总支路数,kj为对应支路的开关状态,rj为支路电阻,Pij为流过该支路的有功功率,Qij为流过该支路的无功功率,Uj为该支路末端节点的电压。In the formula, F is the total network loss of the distribution network, b is the total number of branches of the distribution network, k j is the switching state of the corresponding branch, r j is the branch resistance, and P ij is the active power flowing through the branch. , Qi ij is the reactive power flowing through the branch, and U j is the voltage at the end node of the branch.
3.根据权利要求1所述的计及电动汽车参与调频的配电网重构方法,其特征在于所述配电网受到如下约束:3. The distribution network reconfiguration method that takes into account that electric vehicles participate in frequency regulation according to claim 1, is characterized in that described distribution network is subject to following constraints: (1)潮流约束:(1) Power flow constraints:
Figure FDA0002798400520000032
Figure FDA0002798400520000032
Figure FDA0002798400520000033
Figure FDA0002798400520000033
式中,N为节点数,Vi为节点i对应的电压,Gij为支路ij的电导,Bij为支路ij的电纳,θij为节点i与节点j的节点电压相位差;where N is the number of nodes, V i is the voltage corresponding to node i, G ij is the conductance of branch ij, B ij is the susceptance of branch ij, θ ij is the node voltage phase difference between node i and node j; (2)电压电流约束:(2) Voltage and current constraints: Uimin≤Ui≤UimaxU imin ≤U i ≤U imax ; Ii<IimaxI i <I imax ; 式中,Ui为节点i电压,Uimax为节点i的电压上限,Uimax为节点i的电压下限,Ii为流过支路bi的电流,Iimax为支路bi允许通过的最大电流值,Nb为配电网支路矩阵;In the formula, U i is the voltage of node i, U imax is the upper limit of the voltage of node i, U imax is the lower limit of the voltage of node i, I i is the current flowing through the branch b i , and I imax is the allowable passage of the branch b i . The maximum current value, N b is the distribution network branch matrix; (3)配电线路传输容量约束:(3) Transmission capacity constraints of distribution lines: Pj 2+Qj 2≤0.8·SjmaxP j 2 +Q j 2 ≤0.8·S jmax ; 式中,Pj为第j条支路流过的有功功率,Qj为第j条支路流过的无功功率,Sjmax为该条线路的额定容量;In the formula, P j is the active power flowing through the jth branch, Q j is the reactive power flowing through the jth branch, and S jmax is the rated capacity of the line; (4)电动汽车负荷调频备用约束:(4) Electric vehicle load frequency regulation reserve constraints: g∈Gcon g∈Gcon ;
Figure FDA0002798400520000041
Figure FDA0002798400520000041
式中,g为当前配电网网架结构;Gcon为无回路配电网网络架构;Gisl为无孤岛配电网网络架构。In the formula, g is the current distribution network structure; G con is the loop-free distribution network structure; G isl is the island-free distribution network structure.
4.根据权利要求1所述的计及电动汽车参与调频的配电网重构方法,其特征在于所述目标函数为重构后配电网总网损最小,计算公式为:4. The distribution network reconfiguration method according to claim 1, characterized in that the objective function is that the total network loss of the distribution network after the reconfiguration is minimum, and the calculation formula is:
Figure FDA0002798400520000042
Figure FDA0002798400520000042
式中,F为配电网总网损,b为配电网总支路数,kj为对应支路的开关状态,rj为支路电阻,Pij为流过该支路的有功功率,Qij为流过该支路的无功功率,Uj为该支路末端节点的电压。In the formula, F is the total network loss of the distribution network, b is the total number of branches of the distribution network, k j is the switching state of the corresponding branch, r j is the branch resistance, and P ij is the active power flowing through the branch. , Qi ij is the reactive power flowing through the branch, and U j is the voltage at the end node of the branch.
5.根据权利要求1所述的计及电动汽车参与调频的配电网重构方法,其特征在于所述电动汽车参与调频容量的分布与分配受到如下约束:5. The distribution network reconfiguration method according to claim 1, characterized in that the distribution and distribution of the electric vehicle's participation in frequency regulation are subject to the following constraints: (1)电动汽车负荷参与调频容量的约束:(1) Constraints of electric vehicle load participation in frequency regulation capacity:
Figure FDA0002798400520000051
Figure FDA0002798400520000051
式中,Nev为电动汽车负荷参与调频的节点数;Pnmev为电动汽车负荷在对应节点提供的调频容量;Pzrl为系统需要电动汽车负荷提供的调频总容量;In the formula, N ev is the number of nodes that the electric vehicle load participates in frequency regulation; P nmev is the frequency regulation capacity provided by the electric vehicle load at the corresponding node; P zrl is the total frequency regulation capacity provided by the electric vehicle load required by the system; (2)电动汽车负荷调频备用约束:(2) Electric vehicle load frequency regulation reserve constraints: PV2G≤PV2GmaxP V2G ≤ P V2Gmax ; 式中,PV2G为电动汽车负荷提供的调频备用;PV2Gmax为电动汽车负荷调频备用上限。In the formula, P V2G is the frequency regulation reserve provided by the electric vehicle load; P V2Gmax is the upper limit of the frequency regulation reserve of the electric vehicle load.
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