CN114024338B - Large-scale wind power collection grid split-phase power flow optimization method and system - Google Patents
Large-scale wind power collection grid split-phase power flow optimization method and system Download PDFInfo
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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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/381—Dispersed generators
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- 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
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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
- H02J3/12—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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- 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
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- 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 relates to a split-phase power flow optimization method and a split-phase power flow optimization system for a large-scale wind power collection grid, which belong to the technical field of wind power generation, wherein a split-phase power flow optimization model for optimizing control from a system level is constructed, a Pareto archive multi-target particle swarm optimization algorithm is utilized for optimizing and solving the split-phase power flow optimization model, and the compensation capacity of each phase of a collection station in the large-scale wind power collection grid and reactive power compensation devices of all wind power plants at each wind speed is obtained, so that the voltage unbalance degree of all optimized nodes in the large-scale wind power collection grid can meet the requirement, and the three-phase voltage unbalance phenomenon of a wind power collection area is effectively inhibited at the system level from the global angle.
Description
Technical Field
The invention relates to the technical field of wind power generation, in particular to a split-phase tide optimization method and system for a large-scale wind power collection grid.
Background
Three-phase voltage unbalance phenomenon often occurs in large-scale wind power centralized grid-connected areas, and even when the phenomenon is serious, a fan is disconnected, so that the safe and stable operation of a power system is greatly adversely affected. At present, the research of current optimization of a wind power collection area is concentrated on economic problems such as minimum network loss, minimum total capacity of a compensation device, and system safety problems such as minimum node voltage deviation, but the research of improving the three-phase voltage unbalance problem of the wind power collection area from the system level through current optimization is lacking, so that a split-phase current optimization model for inhibiting the voltage unbalance of a wind power plant is established, and an algorithm capable of accurately solving the optimization model to obtain the compensation capacity of each phase of each wind power plant and the reactive power compensation device of a collection station is introduced, so that the method has important significance and engineering value.
Disclosure of Invention
The invention aims to provide a split-phase tide optimization method and system for a large-scale wind power collection grid, which can effectively inhibit the phenomenon of three-phase voltage unbalance of a wind power collection area from a global angle in a system level.
In order to achieve the above object, the present invention provides the following solutions:
a split-phase power flow optimization method for a large-scale wind power collection grid, the method comprising:
constructing a split-phase tide optimization model of a large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions; the objective function comprises a minimum negative sequence voltage unbalance mean objective function and a minimum voltage deviation objective function;
acquiring line parameters, load data and output power data of a wind power plant of a large-scale wind power collection grid at different wind speeds;
and solving the split-phase power flow optimization model by adopting a Pareto archive multi-objective particle swarm optimization algorithm according to line parameters, load data and wind farm output power data of the large-scale wind power collection grid at each wind speed to obtain the compensation capacity of each phase of the reactive power compensation device of the collection station and each wind farm in the large-scale wind power collection grid at each wind speed.
Optionally, the minimum negative sequence voltage unbalance mean value objective function isWherein F is 1 Is the mean value of the negative sequence voltage unbalance degree, U i1 Is the positive sequence voltage effective value of node i, < ->U i2 Is the negative sequence voltage effective value of node i, < -> And->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes;
the minimum voltage deviation objective function isWherein F is 2 For voltage deviation>Actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
Optionally, the constraint condition includes: equality constraints and inequality constraints.
Optionally, the equality constraint includes: node current equation constraint, branch voltage equation constraint and three-phase power flow equation constraint;
the node current equation is constrained to beWherein (1)>For phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>The phi phase current flowing on the first branch connected with the node j is represented, and p is the number of branches connected between the node i and the node j;
the branch voltage equation is constrained to beWherein (1)>Phi-phase voltage, which represents node i, +.>Branch resistance representing the branch connected between node i and node jAn anti-matrix;
the three-phase tide equation is constrained as
Wherein P is i φ For phi-phase active power of node i, N is the number of nodes of the whole collecting power grid, ++>For m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
Optionally, the inequality constraint includes: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the node voltage safety constraint;
the negative sequence voltage unbalance degree constraint is that
The upper limit and the lower limit of the compensation capacity of the reactive compensation device are constrained as followsWherein (1)> Reactive compensation installed separatelyThe reactive lower limit and the reactive upper limit of the phi phase of the device can be compensated;
the voltage safety constraint of the node is thatWherein (1)>The phi-phase voltages at node i, respectively, allow a lower and an upper limit.
A split-phase power flow optimization system for a large-scale wind power collection grid, the system comprising:
the split-phase tide optimization model construction module is used for constructing a split-phase tide optimization model of the large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions; the objective function comprises a minimum negative sequence voltage unbalance mean objective function and a minimum voltage deviation objective function;
the input data acquisition module is used for acquiring line parameters, load data and output power data of the wind power plant of the large-scale wind power collection grid at different wind speeds;
the compensation capacity obtaining module is used for solving the split-phase power flow optimization model by adopting a Pareto archive multi-objective particle swarm optimization algorithm according to line parameters, load data and wind power plant output power data of the large-scale wind power collection grid at each wind speed to obtain the compensation capacity of each phase of the reactive power compensation device of the collection station and each wind power plant in the large-scale wind power collection grid at each wind speed.
Optionally, the minimum negative sequence voltage unbalance mean value objective function isWherein F is 1 Is the mean value of the negative sequence voltage unbalance degree, U i1 Is the positive sequence voltage effective value of node i, < ->U i2 Is the negative sequence voltage effective value of node i, < -> And->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes;
the minimum voltage deviation objective function isWherein F is 2 For voltage deviation>Actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
Optionally, the constraint condition includes: equality constraints and inequality constraints.
Optionally, the equality constraint includes: node current equation constraint, branch voltage equation constraint and three-phase power flow equation constraint;
the node current equation is constrained to beWherein (1)>For phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>The phi phase current flowing on the first branch connected with the node j is represented, and p is the number of branches connected between the node i and the node j;
the branch voltage equation is constrained to beWherein (1)>Phi-phase voltage, which represents node i, +.>A branch impedance matrix representing a branch connected between node i and node j;
the three-phase tide equation is constrained as
Wherein P is i φ For phi-phase active power of node i, N is the number of nodes of the whole collecting power grid, ++>For m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
Optionally, the inequality constraint includes: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the node voltage safety constraint;
the negative sequence voltage unbalance degree constraint is that
The upper limit and the lower limit of the compensation capacity of the reactive compensation device are constrained as followsWherein (1)> The lower limit and the upper limit of reactive power which can be compensated by phi phase of the installed reactive power compensation device are respectively;
the voltage safety constraint of the node is thatWherein (1)>The phi-phase voltages at node i, respectively, allow a lower and an upper limit.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention discloses a split-phase power flow optimization method and a split-phase power flow optimization system for a large-scale wind power collection grid, which are characterized in that a split-phase power flow optimization model which is optimally controlled from a system level is constructed, the split-phase power flow optimization model is optimally solved by using a Pareto archive multi-target particle swarm optimization algorithm, and the compensation capacity of each phase of a collection station in the large-scale wind power collection grid and reactive power compensation devices of all wind power fields at each wind speed is obtained, so that the voltage unbalance degree of all optimized nodes in the large-scale wind power collection grid can meet the requirement, and the three-phase voltage unbalance phenomenon of a wind power collection area is effectively inhibited at the system level from the global angle.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a split-phase power flow optimization method of a large-scale wind power collection grid;
FIG. 2 is a partial equivalent circuit diagram of a large-scale wind power collection grid provided by the invention;
FIG. 3 is a schematic diagram of a Pareto archive multi-target particle swarm algorithm according to the present invention;
fig. 4 is a network structure diagram of a wind power collection area according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a split-phase tide optimization method and system for a large-scale wind power collection grid, which can effectively inhibit the phenomenon of three-phase voltage unbalance of a wind power collection area from a global angle in a system level.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
The invention provides a split-phase power flow optimization method for a large-scale wind power collection grid, which is shown in figure 1 and comprises the following steps:
step 101, constructing a split-phase tide optimization model of a large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions.
Split-phase tide optimizing model
(1) Objective function
The split-phase power flow optimization model of the large-scale wind power collection grid established by the invention mainly comprises 2 optimization targets. Firstly, aiming at the problem that the voltage unbalance of a large-scale wind power collection area and the negative sequence voltage unbalance of a strong wind working condition are overlarge, the voltage unbalance of each bus after three-phase tide optimization needs to be as small as possible. Secondly, in order to ensure the safety and stability of the power system and have good electric energy quality, the deviation between the actual voltage value and the voltage reference value of the collecting bus and each wind power plant access point should be as small as possible.
The objective functions include a minimum negative sequence voltage imbalance mean objective function and a minimum voltage deviation objective function.
1) Minimum negative sequence voltage imbalance mean objective function
According to national standard GB/T15543, the expression of the voltage unbalance degree can be obtained as follows:
then, the node negative sequence voltage balance degree mean expression for optimization is:
the minimum negative sequence voltage unbalance mean value objective function is
Wherein F is 1 Is the mean value of the negative sequence voltage unbalance degree, U i1 Is the positive sequence voltage effective value of the node i, U i2 Is the negative sequence voltage effective value of the node i,and->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes.
2) The deviation between the actual value of each phase voltage of the optimization node and the voltage reference value is minimum:
the minimum voltage deviation objective function is
Wherein F is 2 In order to be able to vary the voltage,actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
(2) Constraint conditions
The constraint conditions include: equality constraints and inequality constraints.
1) Equation constraint
The equality constraints include: node current equation constraints, branch voltage equation constraints, and three-phase power flow equation constraints.
Assuming that a local equivalent circuit diagram of the large-scale wind power collection grid is shown in fig. 2, according to the local equivalent circuit diagram, the equation constraint conditions to be satisfied by the split-phase power flow optimization model can be listed as follows:
node current equation constraint:
wherein,for phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>Represents the phi phase current flowing on the first branch connected to node j, p is the number of branches connected between node i and node j, +.>Representing the sum of all phi-phase currents flowing on the branch connected to node j.
Branch voltage equation constraint:
in the method, in the process of the invention,
wherein,phi-phase voltage, which represents node i, +.>A branch impedance matrix representing a branch connected between node i and node j; />For the respective phase self-impedance between nodes i, j, < >> Is the transimpedance between the phases of nodes i, j.
Three-phase tide equation constraint:
wherein P is i φ The phi-phase active power of the node i is N is the number of nodes of the whole collecting power grid,for m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
In the view of figure 2,phi phase voltage representing the head node, +.>Represents the apparent power absorbed by the phi-phase of node i from the grid,apparent power absorbed from the grid by phi phase representing node j, < >>Represents the phi phase current flowing on the 1 st branch connected to node j,/>Represents the phi phase current flowing on the mth branch connected to node j,/and>representing the phi-phase voltage of node k.
2) Inequality constraint
Inequality constraints include: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the voltage safety constraint of the node.
Negative sequence voltage imbalance constraint:
the national standard GB/T15543 indicates that under the normal operation condition of the power system, the voltage unbalance degree of the public connection point cannot exceed 2 percent and cannot exceed 4 percent in short time, so that the constraint on the negative sequence voltage unbalance degree of the optimized node is introduced:
the reactive compensation device compensates the upper limit constraint and the lower limit constraint of capacity:
wherein,the lower limit and the upper limit of reactive power which can be compensated by phi phase of the installed reactive power compensation device are respectively;
voltage safety constraint of node:
wherein,the phi-phase voltages at node i, respectively, allow a lower and an upper limit.
And 102, acquiring line parameters, load data and output power data of the wind power plant of the large-scale wind power collection grid at different wind speeds.
And 103, solving a split-phase power flow optimization model by adopting a Pareto archive multi-objective particle swarm optimization algorithm according to line parameters, load data and wind farm output power data of the large-scale wind power collection grid at each wind speed, and obtaining the compensation capacity of each phase of a collection station and a reactive power compensation device of each wind farm in the large-scale wind power collection grid at each wind speed.
The split-phase power flow optimization model of the large-scale wind power collection grid has the characteristics of multiple targets, nonlinearity and multiple constraints, and a Pareto archive multi-target particle swarm optimization algorithm is introduced to perform optimization solving aiming at the characteristics of the provided optimization model.
In the algorithm, a solution in the optimization model of the required solution is represented by particles with no volume in a D-dimensional search space, and this solution can be represented by the velocity vector v i ＝[v i1 ,v i2 ,...,v iD ]And a position vector x i ＝[x i1 ,x i2 ,...,x iD ]Representing, x i Substituting the objective function F in the optimization model 1 (x)，F 2 (x) Then x=x can be found i The function value of each objective function, and all particles constitute a population. Each particle in the population updates its own speed and position through its own and population information, and the update formula is:
where t is the current iteration number and ω is the inertial weight coefficient. The invention adopts linear decreasing inertia weight, namelyWherein omega min 、ω max Respectively, is the maximum value and the minimum value of omega, T is the maximum iteration number, c 1 、c 2 Learning factors of individuals and populations, r 1 And r 2 Is in [0,1 ]]Random number distributed on the upper part, pbest i As the individual optimal position of particle i, gbest is the global optimal position.
Referring to fig. 3, the specific calculation steps are as follows:
step 1: and (3) inputting system parameters: inputting line parameters, load data and wind power plant output power data of a large-scale wind power collection grid;
step 2: algorithm parameter input: setting a population scale N and a learning factor c 1 、c 2 Inertia weight coefficient omega, iteration times T and external file scale N a 。
Step 3: randomly initializing a population: according to the compensation capacity of the reactive compensation device in the optimization model, randomly initializing the initial speed and the position of an individual in the particle population, substituting the initial speed and the position into an objective function as solutions of the optimization model to calculate F 1 (x i )，F 2 (x i ) In the iteration, a solution that all objective function values are not inferior to other particles and at least one objective function value is superior to other particles is used as a non-inferior solution, and the obtained non-inferior solution is added into an external file for storage;
step 4: determining an initial individual optimum position pbest for each particle i An initial global optimum position gbest;
step 4.1: pbest (p best) i Is selected from the group consisting of: all objective function values of the new solution x of the particles are not inferior to the original pbest i Corresponding objective function values and at least one of the objective function values is better than the pbest i At this time, it is called x-dominant pbest i Then take x as the new pbest i Otherwise, pbest i Remain unchanged; if x is equal to the original pbest i Independent of each other, randomly selecting one as a pbest between the two i 。
Step 4.2: selection of gbest: adopting a self-adaptive grid algorithm, forming a target space by the change range of the objective function value of each objective function, equally dividing the target space into a plurality of grids, and enabling all non-inferior solutions in the step 3 to have a position corresponding to the target space; defining fitness values for each grid containing non-bad solutions; selecting a grid by adopting a roulette method; one particle is randomly selected from the grid as gbest.
Step 5: updating the position and speed of each particle according to the formula (2-1), and selecting a new pbest of the particle i ；
Step 6: updating, maintaining and selecting a new gbest mixing process for the external files;
step 6.1: if the number of the non-inferior solutions in the external file is smaller than or equal to a specified value, the newly generated non-inferior solutions x are directly entered into the external file, if x dominates a part of individuals of the external file, x replaces the dominated solution, and at the same time, all particles taking the dominated solution as the gbest select x as the new gbest. If the number of particles in the external file exceeds the file size, selecting a grid with the highest density to randomly select one particle and reject the particle on the basis of obtaining each grid fitness value by using a self-adaptive grid method until the file size is within a required range.
Step 7: judging whether the iteration number reaches the maximum iteration number, if so, terminating the algorithm, and if not, turning to the step 5.
The reactive power optimization concept is applied to improving the electric energy quality, and particularly an optimization model for split-phase power flow optimization is constructed according to the actual problem background of three-phase voltage unbalance of a large-scale wind power collection grid, the optimization model is an optimization model for optimizing control from a system level, and a Pareto archive multi-target particle swarm optimization algorithm is introduced to perform optimization solution. The split-phase power flow optimization method can effectively inhibit the three-phase voltage unbalance phenomenon of the wind power collection area from the global angle at the system level, and is beneficial to the safe and stable operation of the power system under the access of high-proportion new energy.
According to the method for optimizing the split-phase power flow of the large-scale wind power collection grid, which is provided by the invention, the matlab simulation software is utilized to perform split-phase power flow optimization calculation on the matlab simulation software at different wind speeds so as to verify the correctness of the method.
Taking a certain wind power collection area with three-phase voltage imbalance as an example, the network structure of the area is shown in fig. 4.
Assuming that the optimized collection station and the subordinate wind farm are provided with static reactive compensators, the compensation capacity is 30Mvar, and the reactive capacity which can be compensated for each phase is +/-10 Mvar.
When split-phase tide optimization is not performed, the voltage unbalance degree of the collecting station and the subordinate wind power plant under different wind speeds is shown in table 1.
TABLE 1 degree of imbalance in voltage at each node before optimization
Therefore, under different wind speeds, three-phase voltage unbalance phenomena exist in the collecting station and the subordinate wind field, and under the working condition of strong wind, namely, when the wind speed is over 12m/s, the negative sequence voltage unbalance degree exceeds the national standard requirement.
The split-phase power flow optimization algorithm provided by the invention can calculate the reactive power required to be compensated for each phase of the optimization node under different wind speeds, and the reactive power required to be compensated for each phase of the reactive power compensation device installed in the collection station is listed when the wind speeds are 9-13m/s respectively by taking the collection station as an example, as shown in the table 2.
Table 2 reactive power per phase compensation (MVA) for each node
After split-phase tide optimization, the voltage unbalance degree of the collecting station and the subordinate wind power plant under different wind speeds is shown in table 3.
TABLE 3 degree of imbalance in voltage at each node after optimization
The method for optimizing the split-phase power flow of the large-scale wind power collection grid can be seen to well reduce the negative sequence voltage unbalance degree at the collection station and the wind field outlet under different wind speeds.
The invention has the following advantages:
(1) The split-phase power flow optimization method for the large-scale wind power collection grid can effectively inhibit the problem of three-phase voltage unbalance of a collection station and a subordinate wind field under the working condition of strong wind, reduces the node voltage unbalance of all optimization nodes to below 2%, and meets the requirement that the voltage unbalance of the current national standard on PCC points (namely the collection station nodes in the invention) of a power system is not more than 2% and is not more than 4% in short time; the method can lower the unbalance degree of the PCC point negative sequence voltage of the large-scale wind power collection grid meeting national standard requirements under the working condition of small wind, improves the electric energy quality, improves the safety and stability of the large-scale wind power collection grid-connected system, and has a certain reference value for the development of large-scale wind power collection grid-connected system.
(2) The three-phase voltage unbalance degree is reduced, the node voltage reference value can be considered, and the deviation from the node voltage reference value can be as small as possible while the node voltage actual value is within a certain safety requirement range.
The reason for the advantages described above is:
(1) The split-phase power flow optimization model provided by the invention coordinates the split-phase dispatching and collecting station and the reactive compensation device of the subordinate wind field uniformly from the global angle, so that the voltage unbalance degree of all the optimization nodes can meet the requirement.
(2) The invention adds the target with minimum node voltage deviation into the split-phase power flow optimization model, so that the aims of inhibiting three-phase voltage unbalance and reducing the deviation between the actual value and the reference value of the node voltage can be simultaneously achieved.
The invention also provides a split-phase power flow optimizing system of the large-scale wind power collection grid, which comprises the following components:
the split-phase tide optimization model construction module is used for constructing a split-phase tide optimization model of the large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions; the objective function comprises a minimum negative sequence voltage unbalance mean objective function and a minimum voltage deviation objective function;
the input data acquisition module is used for acquiring line parameters, load data and output power data of the wind power plant of the large-scale wind power collection grid at different wind speeds;
the compensation capacity obtaining module is used for solving the split-phase power flow optimization model by adopting a Pareto archive multi-target particle swarm optimization algorithm according to line parameters, load data and wind power plant output power data of the large-scale wind power collection grid at each wind speed, and obtaining the compensation capacity of each phase of the reactive power compensation device of the collection station and each wind power plant in the large-scale wind power collection grid at each wind speed.
The minimum negative sequence voltage unbalance mean value objective function isWherein F1 is the average value of the negative sequence voltage unbalance degree, U i1 Is the positive sequence voltage effective value of node i, < ->U i2 Is the negative sequence voltage effective value of node i, < -> And->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes;
the minimum voltage deviation objective function isWherein F2 is the voltage deviation,actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
The constraint conditions include: equality constraints and inequality constraints.
The equality constraints include: node current equation constraint, branch voltage equation constraint and three-phase power flow equation constraint;
the node current equation is constrained to beWherein (1)>For phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>The phi phase current flowing on the first branch connected with the node j is represented, and p is the number of branches connected between the node i and the node j;
the branch voltage equation is constrained toWherein (1)>Phi-phase voltage, which represents node i, +.>A branch impedance matrix representing a branch connected between node i and node j;
the three-phase tide equation is constrained as
Wherein P is i φ The phi-phase active power of the node i is N, the number of the nodes of the whole collecting power grid is V k m For m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
Inequality constraints include: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the node voltage safety constraint;
the negative sequence voltage unbalance degree is constrained as
The upper limit and the lower limit of the compensation capacity of the reactive compensation device are constrained as followsWherein (1)>The lower limit and the upper limit of reactive power which can be compensated by phi phase of the installed reactive power compensation device are respectively;
the voltage safety constraint of the node is thatWherein (1)>The phi-phase voltages at node i, respectively, allow a lower and an upper limit.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The split-phase power flow optimization method for the large-scale wind power collection grid is characterized by comprising the following steps of:
constructing a split-phase tide optimization model of a large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions; the objective function comprises a minimum negative sequence voltage unbalance mean objective function and a minimum voltage deviation objective function;
acquiring line parameters, load data and output power data of a wind power plant of a large-scale wind power collection grid at different wind speeds;
according to line parameters, load data and output power data of the wind power plant of the large-scale wind power collection grid at each wind speed, solving the split-phase power flow optimization model by adopting a Pareto archive multi-objective particle swarm optimization algorithm to obtain compensation capacity of each phase of a collection station and a reactive power compensation device of each wind power plant in the large-scale wind power collection grid at each wind speed;
the minimum negative sequence voltage unbalance mean value objective function isWherein F is 1 Is the mean value of the negative sequence voltage unbalance degree, U i1 Is the positive of node iSequence voltage valid value, < >>U i2 Is the negative sequence voltage effective value of node i, < -> And->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes;
the minimum voltage deviation objective function isWherein F is 2 In order to be able to vary the voltage,actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
2. The method for optimizing split-phase power flow of a large-scale wind power collection grid according to claim 1, wherein the constraint conditions comprise: equality constraints and inequality constraints.
3. The method for split-phase power flow optimization of a large-scale wind power collection grid according to claim 2, wherein the equality constraint comprises: node current equation constraint, branch voltage equation constraint and three-phase power flow equation constraint;
the node current equation is constrained to beWherein (1)>For phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>The phi phase current flowing on the first branch connected with the node j is represented, and p is the number of branches connected between the node i and the node j;
the branch voltage equation is constrained to beWherein (1)>Phi-phase voltage, which represents node i, +.>A branch impedance matrix representing a branch connected between node i and node j;
the three-phase tide equation is constrained as
Wherein (1)>For phi-phase active power of node i, N is the number of nodes of the whole collecting power grid, ++>For m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
4. A method of split-phase power flow optimization of a large-scale wind power collection grid according to claim 3, wherein the inequality constraints include: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the node voltage safety constraint;
the negative sequence voltage unbalance degree constraint is that
The upper limit and the lower limit of the compensation capacity of the reactive compensation device are constrained as followsWherein (1)> The lower limit and the upper limit of reactive power which can be compensated by phi phase of the installed reactive power compensation device are respectively;
the voltage safety constraint of the node is thatWherein (1)>The phi-phase voltages at node i, respectively, allow a lower and an upper limit.
5. A split-phase power flow optimization system for a large-scale wind power collection grid, the system comprising:
the split-phase tide optimization model construction module is used for constructing a split-phase tide optimization model of the large-scale wind power collection grid; the split-phase power flow optimization model comprises an objective function and constraint conditions; the objective function comprises a minimum negative sequence voltage unbalance mean objective function and a minimum voltage deviation objective function;
the input data acquisition module is used for acquiring line parameters, load data and output power data of the wind power plant of the large-scale wind power collection grid at different wind speeds;
the compensation capacity obtaining module is used for solving the split-phase power flow optimization model by adopting a Pareto archive multi-objective particle swarm optimization algorithm according to line parameters, load data and wind power plant output power data of the large-scale wind power collection grid at each wind speed to obtain the compensation capacity of each phase of a collection station and reactive power compensation devices of each wind power plant in the large-scale wind power collection grid at each wind speed;
the minimum negative sequence voltage unbalance mean value objective function isWherein F is 1 Is the mean value of the negative sequence voltage unbalance degree, U i1 Is the positive sequence voltage effective value of node i, < ->U i2 Is the negative sequence voltage effective value of node i, < -> And->The voltage effective values of a phase, b phase and c phase of the node i are respectively represented, alpha is a twiddle factor, and n is the total number of nodes;
the minimum voltage deviation objective function isWherein F is 2 In order to be able to vary the voltage,actual value of node voltage for the phi-th phase of node i, for example>The node voltage reference value for the phi-th phase of node i.
6. The split-phase power flow optimization system of a large-scale wind power collection grid according to claim 5, wherein the constraint condition comprises: equality constraints and inequality constraints.
7. The large-scale wind power collection grid split-phase power flow optimization system of claim 6, wherein the equality constraint comprises: node current equation constraint, branch voltage equation constraint and three-phase power flow equation constraint;
the node current equation is constrained to beWherein (1)>For phi phase current flowing on the branch connected between node i and node j, a, b and c are respectively a phase, b phase and c phase in three phases, < >>Represents the phi-phase active power of node j, < +.>Phi-phase reactive power, which represents node j, +.>Reactive power representing phase compensation of the reactive compensation means phi of node j +.>Phi-phase voltage, which represents node j, +.>The phi phase current flowing on the first branch connected with the node j is represented, and p is the number of branches connected between the node i and the node j;
the branch voltage equation is constrained to beWherein (1)>Phi-phase voltage, which represents node i, +.>A branch impedance matrix representing a branch connected between node i and node j;
the three-phase tide equation is constrained as
Wherein (1)>For phi-phase active power of node i, N is the number of nodes of the whole collecting power grid, ++>For m-phase voltage of node k, G ik,φm For the conductance between the phi and m phases of nodes i, k, theta ik,φm B is the voltage phase angle difference between phi and m phases of the node i and the node k ik,φm For susceptances between phi and m phases of node i, node k, ++>Phi-phase reactive power, which represents node i, +.>And representing the reactive power compensated by the node i reactive power compensation device phi.
8. The large scale wind power collection grid split-phase power flow optimization system of claim 7, wherein the inequality constraint comprises: the negative sequence voltage unbalance degree constraint, the reactive compensation device compensates the capacity upper limit constraint and the capacity lower limit constraint and the node voltage safety constraint;
the negative sequence voltage unbalance degree constraint is that
The reactive powerThe upper limit and the lower limit of the compensation capacity of the compensation device are constrained asWherein (1)> The lower limit and the upper limit of reactive power which can be compensated by phi phase of the installed reactive power compensation device are respectively;
the voltage safety constraint of the node is thatWherein (1)>The phi-phase voltages at node i, respectively, allow a lower and an upper limit.
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