CN110912177A - Multi-objective optimization design method for multi-terminal flexible direct current power transmission system - Google Patents
Multi-objective optimization design method for multi-terminal flexible direct current power transmission 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
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- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
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Abstract
The invention discloses a multi-objective optimization design method for a multi-terminal flexible direct current transmission system, belongs to the technical field of power transmission, and aims to solve the problems that a single objective system strategy cannot periodically adjust a control instruction of a converter station in the multi-terminal flexible direct current transmission system, and the direct current voltage and the active power of the converter station are not accurate enough. The method comprises the steps of building a six-terminal flexible direct-current transmission system in power system transient simulation software, setting a control mode and initial parameters of the direct-current transmission system, building a multi-target power flow optimization model, solving the multi-target power flow optimization model, inputting direct-current voltage and active power in a multi-target optimization result into corresponding converter station operation models in the multi-terminal flexible direct-current transmission system built in the step one, obtaining control parameters of the converter stations through optimal power flow calculation at regular time and issuing the control parameters. The method has the advantages of being scientific and reasonable, strong in applicability, good in effect and the like.
Description
Technical Field
The invention belongs to the technical field of power transmission, and particularly relates to a multi-objective optimization design method for a multi-terminal flexible direct current power transmission system.
Background
The northwest China is flat in topography, rare in population and rich in wind energy and solar energy resources, so that the method becomes the preferred site for construction and installation of large land wind power plants. By 2018, the wind power installation capacity of the spring city reaches 915 ten thousand kilowatts, the photovoltaic power generation installation capacity reaches 200 ten thousand kilowatts, and the new energy installation capacity is expected to reach 2000 ten thousand kilowatts in 2020. However, large-scale new energy power generation bases established in the area are far away from the load center, resources and loads are reversely distributed, and generated power cannot be consumed on site. In order to relieve the contradiction of energy supply and demand, the Jiuquan-Hunan +/-800 kilovolt high-voltage direct-current transmission project which is put into use in 2017 directly transmits new energy electric power of Gansu province to Hunan, and the maximum electric power transmission quantity per year can reach 400 hundred million kilowatts. However, the stochastic and intermittent nature of wind energy makes wind control difficult, and large scale integration can lead to serious operational and reliability problems.
The new generation of flexible direct current transmission technology has unique technical advantages in solving the problems of new energy power generation dispersion, miniaturization, load center distance and the like. The ports of the converter stations are connected with the wind power plants, the photovoltaic power stations and the alternating current system, so that a flexible and controllable multi-terminal flexible direct current transmission system can be formed, the research is widely carried out on the aspects of new energy grid connection, wide-area asynchronous interconnection of a large power grid and the like, and particularly, the technical problems of coordination control strategy, tide distribution, economic operation optimization and the like of large-scale new energy access multi-terminal flexible direct current transmission system are solved, and the method has wide application prospect and important research significance.
Based on the advantages of the flexible direct current transmission system in the aspects of black start capability, flexible power control, occupied area and the like, the multi-terminal flexible direct current transmission system can be used as a prospective solution for integrating large-scale new energy, and a direct current power grid formed by the flexible direct current transmission system builds a bridge of each new energy field group, a local alternating current power grid and an outgoing channel. The wind power station, the photovoltaic power station and the alternating current system are jointly networked to form a multi-terminal flexible direct current transmission system, new energy is transmitted to a remote load center through a transmission corridor, the new energy is collected and supported on a local power grid, the power collection and concentrated transmission capacity of a large new energy base, namely a wine spring, can be greatly improved, the transmission cost is reduced, and the optimal configuration of power resources among regional power grids is realized.
The optimal power flow of the power system is an important means for ensuring the safe and economic operation of the system. With the rapid development of an alternating current-direct current hybrid power grid, the access of large-scale clean energy and the wide use of power electronic devices in a power system, the operation characteristics of the system are more complex and changeable, and the traditional single-target optimal power flow cannot meet the actual requirements of multi-target coordinated optimization operation of the system. Due to the fact that the multi-target optimal power flow can effectively solve the problem that the multiple targets with different importance and even conflicting with each other are contained, the result has guiding significance for giving of the control parameters of the multi-terminal flexible direct current transmission system, and reasonable optimization can be better conducted on the operation mode of the system.
In practice, droop control contains 3 control parameters: if only one of the droop coefficient, the direct-current voltage reference value and the direct-current power reference value is designed, the operation characteristics of the whole direct-current power grid are difficult to meet the requirements when the load has large fluctuation. Aiming at the technical problem, the technical personnel in the field provide a multi-objective optimization design method of a multi-terminal flexible direct current power transmission system, which is used for comprehensively coordinating the operating characteristics of the whole multi-terminal flexible direct current power transmission system, integrally improving the reliability of the system and being applied when a control instruction is formulated on a long time scale.
Disclosure of Invention
The invention aims to provide a multi-objective optimization design method for a multi-terminal flexible direct current transmission system, and aims to solve the problems that a single objective system strategy cannot periodically adjust a control instruction of a converter station in the multi-terminal flexible direct current transmission system, and the direct current voltage and the active power of the converter station are not accurately controlled.
In order to solve the problems, the technical scheme of the invention is as follows:
a multi-objective optimization design method for a multi-terminal flexible direct current power transmission system comprises the following steps:
the method comprises the following steps: the method comprises the following steps of (1) building a six-terminal flexible direct current transmission system in power system transient simulation software:
the six-terminal flexible direct-current transmission system mainly comprises two wind power plants, a photovoltaic power station, six converter stations, three alternating-current systems and a direct-current transmission line;
specifically, the method comprises the following steps: the wind power plant 1 is connected with the converter station 1, the wind power plant 2 is connected with the converter station 2, the photovoltaic power station is connected with the converter station 3, the three alternating current systems are respectively connected with the converter station 4, the converter station 5 and the converter station 6, the converter station 1, the converter station 2, the converter station 3, the converter station 4, the converter station 5 and the converter station 6 are connected with one another, and all the connections adopt direct current transmission lines.
Step two: setting a control mode and initial parameters of the direct current transmission system:
the converter station at the power generation side adopts constant alternating voltage control, and the converter station at the power grid side adopts droop control.
Step three: establishing a multi-target power flow optimization model:
the concrete model comprises an objective function, equality constraint and inequality constraint.
Step four: solving a multi-target power flow optimization model:
in the solution of the optimization model, calculation is carried out according to new energy power generation prediction data with the resolution of 15min, the multi-objective power flow optimization is optimized and solved by adopting an NSGA-III algorithm to obtain a group of optimal Pareto solution sets, then the fuzzy membership function is used for carrying out descaler on each objective function, and the degree of satisfaction corresponding to each objective function is calculated.
The non-dominated solution with the standardized maximum membership degree is the optimal compromise solution;
step five: and D, inputting the direct-current voltage and the active power in the multi-objective optimization result into corresponding converter stations in the multi-terminal flexible direct-current transmission system set up in the step one to operate the models.
Step six: and regularly obtaining control parameters of each converter station through optimal power flow calculation and issuing the control parameters:
further, the objective function in step three includes:
1) network loss minimum objective function:
wherein the content of the first and second substances,P loss,lineandP loss,VSCrepresenting losses of the dc pole line and the converter station, respectively;
R i is a lineiThe polar line resistance of (1), namely the sum of the direct current smoothing reactor and the line equivalent resistance;
I i is a lineiThe direct current of (2);I iCis as followsiThe current of each VSC;
a,b,cfor the loss factor of the converter station, the calculation formula is as follows:
wherein the content of the first and second substances,S NandU Nthe rated capacity and the rated voltage of the direct current side of the VSC are respectively;
S BandU Breference capacity and reference voltage of the MTDC system, respectively;
cthe subscripts R and I of (a) correspond to the commutation state and the inversion state of the converter station, respectively.
2) Voltage deviation objective function:
wherein the content of the first and second substances,U dis the voltage offset;
U i.refis a nodeiA desired voltage value of;
U i.maxandU i.minare respectively nodesiVoltage maximum and minimum values of (c).
3) System quiescent voltage stability margin objective function:
wherein the content of the first and second substances,v SMthe static voltage stability margin of the system is set;
λ minis the minimum singular value of the jacobian matrix of the conventional convergent trend.
Furthermore, the constraint condition of the equation in the third step is an alternating current and direct current power balance equation, and the expression is as follows:
wherein the content of the first and second substances,P iGandQ iGare respectively the firstiActive output and reactive output of each node;
P iDandQ iDare respectively nodesiActive load and reactive load of (1);
G ij andB ij are respectively nodesiAnd nodejConductance and susceptance between.
Further, the inequality constraint conditions in the third step comprise node direct current constraint, converter station active power constraint and direct current line current constraint;
wherein the content of the first and second substances,U i,minandU i,maxare respectively nodesiThe lower limit value and the upper limit value of the direct current voltage are respectively 0.9 and 1.1 times of rated voltage values;
P i,minto allow maximum active power to flow through the converter station;
I maxthe maximum current allowed to flow on the dc line.
Further, the detailed steps of the solution in step four are as follows:
(1) initializing relevant parameters of the NSGA-III algorithm, such as crossing rate, variation rate, crossing parameters and variation parameters; setting the scale of the population, the iteration times, the upper limit and the lower limit of a control variable and the like;
(2) generating a certain number of reference points which are uniformly distributed according to the number of the equant parts on each dimension of the target and the number of the target functions;
(3) initializing a population according to the constraint conditions of the decision variables, and setting the iteration times t as 0;
(4) calculating the fitness value of each individual in the population;
(5) generating a progeny population through crossing and mutation operations, and calculating the fitness value of each individual in the progeny population;
(6) combining the parent population and the offspring population to form a new population;
(7) performing rapid non-dominant sorting on the combined population to obtain a non-dominant layer;
(8) selecting the individuals with lower non-dominant layers into a next generation population until all the individuals on the ith layer are selected to the next generation population, and enabling the size of the next generation population to be equal to N;
(9) carrying out normalization processing on individuals in the previous i layer to make the values of the individuals be numbers between [0 and 1 ];
(10) calculating the vertical distance between each individual in the previous i layer and all reference points, finding out the reference point related to each individual, and if the vertical distance between the individual and a certain reference point is shortest, considering that the individual is related to the reference point; calculating the niche of the jth reference point;
(11) selecting K individuals from the i layer to enter a next generation population, so that the population size is just N;
(12) the iteration number is increased by 1, namely t is t + 1;
(13) judging whether a preset iteration number is reached, if so, terminating the algorithm; otherwise, repeating the step 5 to the step 12;
(14) outputting a non-dominated solution set of the high-dimensional target power flow optimization;
(15) and solving the optimal compromise solution of the multi-objective power flow optimization by using a fuzzy group decision method.
And further, in the sixth step, control parameters of all the converter stations are obtained by optimal power flow calculation every 15min at regular time and are issued.
The invention has the following beneficial effects:
the optimization control strategy provided by the invention comprehensively considers a plurality of targets of system power flow optimization, including network loss, voltage deviation, system static voltage stability margin and the like, by acquiring voltage, power signals and wind power prediction data of a direct current power grid, takes direct current voltage of each converter station and active power of each converter station in an optimization result as reference values of a multi-terminal flexible direct current transmission system, and periodically adjusts control instructions of the converter stations in the multi-terminal flexible direct current transmission system to realize optimal direct current power flow of the direct current power grid in a steady state, accurately controls the direct current voltage and the active power of the converter stations and ensures that the direct current power grid operates in an optimal power flow state. Has the advantages of scientific and reasonable method, strong applicability, good effect and the like.
Drawings
Fig. 1 is a schematic structural diagram of a six-terminal flexible direct current transmission system;
fig. 2 is a schematic structural diagram of a six-terminal flexible direct current transmission system in a specific embodiment.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
A multi-objective optimization design method for a multi-terminal flexible direct current power transmission system comprises the following steps:
the method comprises the following steps: the method comprises the following steps of (1) building a six-terminal flexible direct current transmission system in power system transient simulation software:
as shown in fig. 1: the six-end flexible direct-current transmission system mainly comprises 2 wind power plants, 1 photovoltaic power station, 6 converter stations (MMC 1-MMC 6), an alternating-current system and a direct-current transmission line.
Specifically, the method comprises the following steps: the wind power plant 1 is connected with the converter station 1, the wind power plant 2 is connected with the converter station 2, the photovoltaic power station is connected with the converter station 3, two alternating current systems are respectively connected with the converter station 4 and the converter station 5, in order to utilize the Qishao +/-800 kV extra-high voltage direct current transmission project which is running at present, the MMC6 is directly connected into the extra-high voltage direct current transmission system (LCC-HVDC) from the alternating current system, so that the local large-scale new energy power is transmitted to a load center (as shown in figure 2) through an extra-high voltage transmission channel, the converter station 1, the converter station 2, the converter station 3, the converter station 4, the converter station 5 and the converter station 6 are connected with one another, and all the connections adopt direct current transmission lines.
Step two: setting a control mode and initial parameters of the direct current transmission system:
in order to fully absorb large-scale new energy, in the control strategy of the invention, a power generation side converter station (MMC 1-MMC 3) adopts constant alternating voltage control to provide stable alternating voltage for a wind power plant so as to ensure the control effect of a fan, and simultaneously all generated new energy power is injected into a multi-terminal flexible direct current power transmission system (the whole system in figure 1); for the grid side converter stations (MMC 4-MMC 6), however, droop control is used, the purpose of which is to regulate the dc voltage and share power fluctuations.
Step three: establishing a multi-target power flow optimization model:
the concrete model comprises an objective function, equality constraint and inequality constraint:
(1) objective function
1) Network loss minimum objective function:
wherein the content of the first and second substances,P loss,lineandP loss,VSCrepresenting losses of the dc pole line and the converter station, respectively;
R i is a lineiThe polar line resistance of (1), namely the sum of the direct current smoothing reactor and the line equivalent resistance;
I i is a lineiThe direct current of (2);I iCis as followsiThe current of each VSC;
a,b,cfor the loss factor of the converter station, the calculation formula is as follows:
wherein the content of the first and second substances,S NandU Nthe rated capacity and the rated voltage of the direct current side of the VSC are respectively;
S BandU Breference capacity and reference voltage of the MTDC system, respectively;
cthe subscripts R and I of (a) correspond to the commutation state and the inversion state of the converter station, respectively.
2) Voltage deviation objective function:
wherein the content of the first and second substances,U dis the voltage offset;
U i.refis a nodeiA desired voltage value of;
U i.maxandU i.minare respectively nodesiVoltage maximum and minimum values of (c).
3) System quiescent voltage stability margin objective function:
wherein the content of the first and second substances,v SMthe static voltage stability margin of the system is set;
λ minis the minimum singular value of the jacobian matrix of the conventional convergent trend.
When the system operation mode or the network structure changes, the minimum eigenvalue and singular value of the matrix change. The minimum singular value of the jacobian matrix can reflect the voltage stability margin of the system in the current operation state of the system, the smaller the value of the jacobian matrix is, the smaller the stability margin of the system is, and the larger the value of the jacobian matrix is, the larger the stability margin of the system is.
(2) Equality constraint
The equality constraint condition is an alternating current and direct current power balance equation, and the expression is as follows:
wherein the content of the first and second substances,P iGandQ iGare respectively the firstiActive output and reactive output of each node;
P iDandQ iDare respectively nodesiActive load and reactive load of (1);
G ij andB ij are respectively nodesiAnd nodejConductance and susceptance between.
(3) Inequality constraint condition
The inequality constraint conditions comprise node direct current constraint, converter station active power constraint and direct current line current constraint:
wherein the content of the first and second substances,U i,minandU i,maxare respectively nodesiThe lower limit value and the upper limit value of the direct current voltage are respectively 0.9 and 1.1 times of rated voltage values;
P i,minto allow maximum active power to flow through the converter station;
I maxthe maximum current allowed to flow on the dc line.
Step four: solving a multi-target power flow optimization model:
in the solution of the optimization model, calculation is carried out according to new energy power generation prediction data with the resolution of 15min, the multi-objective power flow optimization is optimized and solved by adopting an NSGA-III algorithm to obtain a group of optimal Pareto solution sets, then the fuzzy membership function is used for carrying out descaler on each objective function, and the degree of satisfaction corresponding to each objective function is calculated.
The non-dominated solution with the normalized maximum membership is the optimal trade-off solution: the detailed steps of the solution are as follows:
(1) initializing relevant parameters of the NSGA-III algorithm, such as crossing rate, variation rate, crossing parameters and variation parameters; setting the scale of the population, the iteration times, the upper limit and the lower limit of a control variable and the like;
(2) generating a certain number of reference points which are uniformly distributed according to the number of the equant parts on each dimension of the target and the number of the target functions;
(3) initializing a population according to the constraint conditions of the decision variables, and setting the iteration times t as 0;
(4) calculating the fitness value of each individual in the population;
(5) generating a progeny population through crossing and mutation operations, and calculating the fitness value of each individual in the progeny population;
(6) combining the parent population and the offspring population to form a new population;
(7) performing rapid non-dominant sorting on the combined population to obtain a non-dominant layer;
(8) selecting the individuals with lower non-dominant layers into a next generation population until all the individuals on the ith layer are selected to the next generation population, and enabling the size of the next generation population to be equal to N;
(9) carrying out normalization processing on individuals in the previous i layer to make the values of the individuals be numbers between [0 and 1 ];
(10) calculating the vertical distance between each individual in the previous i layer and all reference points, finding out the reference point related to each individual, and if the vertical distance between the individual and a certain reference point is shortest, considering that the individual is related to the reference point; calculating the niche of the jth reference point;
(11) selecting K individuals from the i layer to enter a next generation population, so that the population size is just N;
(12) the iteration number is increased by 1, namely t is t + 1;
(13) judging whether a preset iteration number is reached, if so, terminating the algorithm; otherwise, repeating the step 5 to the step 12;
(14) outputting a non-dominated solution set of the high-dimensional target power flow optimization;
(15) and solving the optimal compromise solution of the multi-objective power flow optimization by using a fuzzy group decision method.
Step five: and D, inputting the direct-current voltage and the active power in the multi-objective optimization result into corresponding converter stations in the multi-terminal flexible direct-current transmission system set up in the step one to operate the models.
So as to ensure that the whole multi-terminal flexible direct current transmission system operates under a given steady-state target.
Step six: and obtaining and issuing control parameters of each converter station by optimal power flow calculation every 15 min.
The provided optimization control calculation is completed within 15min, the latest new energy output power prediction data can be used all the time, the optimal control mode and control parameters of each converter station in a future alternating current-direct current hybrid system are continuously obtained in a rolling calculation mode, and the real-time performance of the optimal power flow in application is met.
Claims (6)
1. A multi-objective optimization design method for a multi-terminal flexible direct current power transmission system is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: the method comprises the following steps of (1) building a six-terminal flexible direct current transmission system in power system transient simulation software:
the six-terminal flexible direct-current transmission system mainly comprises two wind power plants, a photovoltaic power station, six converter stations, three alternating-current systems and a direct-current transmission line;
specifically, the method comprises the following steps: the wind power plant 1 is connected with the converter station 1, the wind power plant 2 is connected with the converter station 2, the photovoltaic power station is connected with the converter station 3, the three alternating current systems are respectively connected with the converter station 4, the converter station 5 and the converter station 6, the converter station 1, the converter station 2, the converter station 3, the converter station 4, the converter station 5 and the converter station 6 are connected with one another, and all the connections adopt direct current transmission lines;
step two: setting a control mode and initial parameters of the direct current transmission system:
the converter station at the power generation side adopts constant alternating voltage control, and the converter station at the power grid side adopts droop control;
step three: establishing a multi-target power flow optimization model:
the specific model comprises an objective function, equality constraint and inequality constraint;
step four: solving a multi-target power flow optimization model:
in the solution of the optimization model, calculation is carried out according to new energy power generation prediction data with the resolution of 15min, multi-objective power flow optimization is optimized and solved by adopting an NSGA-III algorithm to obtain a group of optimal Pareto solution sets, then the fuzzy membership function is used for carrying out descaler on each objective function, and the degree of satisfaction corresponding to each objective function is calculated;
the non-dominated solution with the standardized maximum membership degree is the optimal compromise solution;
step five: inputting the direct-current voltage and the active power in the multi-objective optimization result into corresponding converter station operation models in the multi-terminal flexible direct-current transmission system set up in the step one;
step six: and obtaining the control parameters of each converter station through optimal power flow calculation at regular time and issuing the control parameters.
2. The multi-objective optimization design method of the multi-terminal flexible direct-current transmission system according to claim 1, characterized by comprising the following steps: the objective function in the third step comprises:
1) network loss minimum objective function:
wherein the content of the first and second substances,P loss,lineandP loss,VSCrepresenting losses of the dc pole line and the converter station, respectively;
R i is a lineiThe polar line resistance of (1), namely the sum of the direct current smoothing reactor and the line equivalent resistance;
I i is a lineiThe direct current of (2);I iCis as followsiThe current of each VSC;
a,b,cfor the loss factor of the converter station, the calculation formula is as follows:
wherein the content of the first and second substances,S NandU Nthe rated capacity and the rated voltage of the direct current side of the VSC are respectively;
S BandU Breference capacity and reference voltage of the MTDC system, respectively;
csubscripts R and I of the converter station respectively correspond to a rectification state and an inversion state of the converter station;
2) voltage deviation objective function:
wherein the content of the first and second substances,U dis the voltage offset;
U i.refis a nodeiA desired voltage value of;
U i.maxandU i.minare respectively nodesiVoltage maximum and minimum values of (d);
3) system quiescent voltage stability margin objective function:
wherein the content of the first and second substances,v SMthe static voltage stability margin of the system is set;
λ minis the minimum singular value of the jacobian matrix of the conventional convergent trend.
3. The multi-objective optimization design method of the multi-terminal flexible direct-current transmission system according to claim 1, characterized by comprising the following steps: the equation constraint conditions in the third step are an alternating current and direct current power balance equation, and the expression is as follows:
wherein the content of the first and second substances,P iGandQ iGare respectively the firstiActive output and reactive output of each node;
P iDandQ iDare respectively nodesiActive load and reactive load of (1);
G ij andB ij are respectively nodesiAnd nodejConductance and susceptance between.
4. The multi-objective optimization design method of the multi-terminal flexible direct-current transmission system according to claim 1, characterized by comprising the following steps: the inequality constraint conditions in the third step comprise node direct current constraint, converter station active power constraint and direct current line current constraint;
wherein the content of the first and second substances,U i,minandU i,maxare respectively nodesiThe lower limit value and the upper limit value of the direct current voltage are respectively 0.9 and 1.1 times of rated voltage values;
P i,minto allow maximum active power to flow through the converter station;
I maxthe maximum current allowed to flow on the dc line.
5. The multi-objective optimization design method of the multi-terminal flexible direct-current transmission system according to claim 1, characterized by comprising the following steps: the detailed steps of the solution in the fourth step are as follows:
(1) initializing relevant parameters of the NSGA-III algorithm, such as crossing rate, variation rate, crossing parameters and variation parameters; setting the scale of the population, the iteration times, the upper limit and the lower limit of a control variable and the like;
(2) generating a certain number of reference points which are uniformly distributed according to the number of the equant parts on each dimension of the target and the number of the target functions;
(3) initializing a population according to the constraint conditions of the decision variables, and setting the iteration times t as 0;
(4) calculating the fitness value of each individual in the population;
(5) generating a progeny population through crossing and mutation operations, and calculating the fitness value of each individual in the progeny population;
(6) combining the parent population and the offspring population to form a new population;
(7) performing rapid non-dominant sorting on the combined population to obtain a non-dominant layer;
(8) selecting the individuals with lower non-dominant layers into a next generation population until all the individuals on the ith layer are selected to the next generation population, and enabling the size of the next generation population to be equal to N;
(9) carrying out normalization processing on individuals in the previous i layer to make the values of the individuals be numbers between [0 and 1 ];
(10) calculating the vertical distance between each individual in the previous i layer and all reference points, finding out the reference point related to each individual, and if the vertical distance between the individual and a certain reference point is shortest, considering that the individual is related to the reference point; calculating the niche of the jth reference point;
(11) selecting K individuals from the i layer to enter a next generation population, so that the population size is just N;
(12) the iteration number is increased by 1, namely t is t + 1;
(13) judging whether a preset iteration number is reached, if so, terminating the algorithm; otherwise, repeating the step 5 to the step 12;
outputting a non-dominated solution set of the high-dimensional target power flow optimization;
and solving the optimal compromise solution of the multi-objective power flow optimization by using a fuzzy group decision method.
6. The multi-objective optimization design method of the multi-terminal flexible direct-current transmission system according to claim 1, characterized by comprising the following steps: and in the sixth step, the control parameters of each converter station are obtained by optimal load flow calculation every 15min at regular time and are issued.
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| CN112446533A (en) * | 2020-09-29 | 2021-03-05 | 东北电力大学 | Multi-target planning method for AC/DC hybrid power distribution network |
| CN113839405A (en) * | 2021-08-09 | 2021-12-24 | 山东大学 | Parameter optimization method and system for new energy island to be sent out through flexible direct current power grid |
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