CN109193634B - Island power grid operation optimization method and system based on multi-terminal flexible direct current - Google Patents

Island power grid operation optimization method and system based on multi-terminal flexible direct current Download PDF

Info

Publication number
CN109193634B
CN109193634B CN201811139182.5A CN201811139182A CN109193634B CN 109193634 B CN109193634 B CN 109193634B CN 201811139182 A CN201811139182 A CN 201811139182A CN 109193634 B CN109193634 B CN 109193634B
Authority
CN
China
Prior art keywords
power
reactive power
direct current
reactive
optimization
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811139182.5A
Other languages
Chinese (zh)
Other versions
CN109193634A (en
Inventor
钟宇军
李程
陆丹丹
徐良军
彭明伟
丁晓宇
宁康红
王晓辉
李澍
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
China Energy Engineering Group Zhejiang Electric Power Design Institute Co ltd
Shandong University
Zhoushan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Original Assignee
China Energy Engineering Group Zhejiang Electric Power Design Institute Co ltd
Shandong University
Zhoushan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by China Energy Engineering Group Zhejiang Electric Power Design Institute Co ltd, Shandong University, Zhoushan Power Supply Co of State Grid Zhejiang Electric Power Co Ltd filed Critical China Energy Engineering Group Zhejiang Electric Power Design Institute Co ltd
Priority to CN201811139182.5A priority Critical patent/CN109193634B/en
Publication of CN109193634A publication Critical patent/CN109193634A/en
Application granted granted Critical
Publication of CN109193634B publication Critical patent/CN109193634B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a method and a system for optimizing the operation of an island power grid based on multi-end flexible direct current, wherein the method comprises the following steps: establishing an alternating current power grid voltage optimization model based on multi-terminal flexible direct current, wherein an objective function of the optimization model comprehensively considers the network loss cost and the reactive power regulating quantity cost; the active power and the reactive power injected into the alternating current system by the converter are taken as control variables, meanwhile, the control variables also comprise the switching group number of compensation capacitors, and the state variables are the output of the generator and the voltage of each node of the system; the coding mode, the mutation operator and the constraint condition processing method of the traditional genetic algorithm are improved, an improved genetic algorithm solving optimization model is provided, the solved result is subjected to simulation analysis, and the benefits of the model are comprehensively evaluated. The work of the model shows that the model has certain significance for the reactive power optimization of the flexible-straight system and has good application prospect.

Description

Island power grid operation optimization method and system based on multi-terminal flexible direct current
Technical Field
The invention relates to the technical field of power grid operation, in particular to a method and a system for optimizing the operation of an island power grid based on multi-terminal flexible direct current.
Background
Island-in-sea electrical networks tend to present more difficulties in operation than traditional continental electrical networks. If the grid structure is relatively weak, the adjustment margin is limited; the reactive charging power on the submarine cable lines is usually large, affecting the voltage level in the system; obvious load peak valley, variable operation modes and the like. Therefore, the dispatching and operation of the island power grid are often more difficult, and are usually accompanied by more frequent reactive power compensation device switching, which causes additional economic loss. Therefore, the operation optimization problem of the island power grid is worthy of deep research.
Various studies have been conducted by many researchers on the optimization problem of the conventional power grid. Some documents optimize the operation mode of the system by adjusting the power output of a power supply (a synchronous generator and reactive compensation equipment), the tap position of a transformer, line parameters and the like in the power system by means of a genetic algorithm, a particle swarm optimization algorithm, a tabu search algorithm and the like, so as to perform optimal power flow calculation. Part of documents apply the idea of multi-target optimal power flow, and give consideration to the requirements of minimum power generation cost or minimum network loss, best static voltage stability, maximum available transmission capacity and the like, and also give consideration to the economical efficiency and safety of the operation of the power system.
In the aspect of introducing the flexible direct current transmission technology into the voltage optimization of an alternating current and direct current system, researchers at home and abroad also carry out pioneering research and provide different models and algorithms. If the method is based on a flexible and straight steady-state model and a control mode, an interior point method is adopted to perform optimal power flow calculation on the alternating current and direct current system; based on a differential evolution algorithm and a prime-dual interior point method, a unified mixed iterative algorithm is provided to solve the optimal power flow of the alternating current-direct current system; the method comprises the steps of taking the minimum network loss of the whole alternating current and direct current system as a target, providing an alternating current and direct current system optimal power flow algorithm containing multi-terminal flexible direct current transmission, and analyzing algorithm performance under the conditions of different VSC control strategies and wind power stations. In addition, in order to solve the characteristic of flexible direct current, a multi-target optimal power flow model of an alternating current-direct current system is constructed by taking minimum active network loss, the best voltage level, the maximum static voltage stability degree of the system and the maximum power supply capacity as optimization targets.
However, most of the current research methods are limited to theoretically optimizing the voltage of the ac/dc system containing the flexible dc, and further research is needed on the positive influence of the flexible dc transmission system on the economy of the ac/dc power grid, especially on the island power grid on the actual engineering level.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a island power grid operation optimization method based on multi-terminal flexible direct current, compared with the traditional power grid optimization method, the method utilizes the active and reactive power adjusting capacity of VSC, takes the pressure of frequent switching of island power grid reactive power compensation in engineering practice into consideration, introduces the economic cost of reactive power adjustment during system operation mode switching into an optimization target, and improves the solving algorithm to improve the efficiency.
In order to achieve the purpose, the following technical scheme is adopted in the application:
the island power grid operation optimization method based on the multi-terminal flexible direct current comprises the following steps:
establishing an alternating current power grid voltage optimization model based on multi-terminal flexible direct current, wherein an objective function of the optimization model comprises the economic cost required by reactive power regulation besides the traditional network loss index;
the active power and the reactive power injected into the alternating current system by the converter are taken as control variables, the active capacity is actively adjusted by the VSC, and the distribution of the active power among the parallel alternating current and direct current lines is optimized, so that the reactive power flow is indirectly controlled; the reactive power adjusting capability of the VSC is used as a continuously adjustable reactive power compensation means to be put into operation, the switching times of the capacitor bank are optimized, meanwhile, the control variable also comprises the switching group number of the compensation capacitor, and the state variable is the output of the generator and the voltage of each node of the system;
an improved genetic algorithm is utilized to solve an optimization model, considering that compensation capacitance is discrete quantity and flexible and straight active and reactive power is continuous quantity, an integer and real number mixed coding mode is adopted in the solution; introducing a penalty term into the objective function to process inequality constraint conditions; and a variation algorithm which gradually decreases along with the increase of evolution algebra and does not exceed a preset minimum variation rate is adopted.
In the technical scheme, the multi-terminal flexible direct current-based alternating current power grid voltage optimization model considers that certain economic cost needs to be paid for the switching operation of the reactive power compensation equipment when the system operation mode is switched, and the objective function of the optimization model also comprises the economic cost needed by reactive power regulation besides the traditional network loss index.
The reactive power adjusting capability of the VSC is used as a reactive power compensation means capable of being continuously adjusted to be put into operation, and the switching times of the capacitor bank are optimized, so that the flexibility of voltage control is improved, and the voltage level is improved.
Further, the objective function in the optimization model is represented by the following formula:
min F＝ατ(ΔPac+ΔPdc)+hcΔx
in the formula, alpha is the price of electric energy, tau is the optimized time length, delta PacFor ac active losses, Δ PdcFor direct current active losses, hcIn order to adjust the cost coefficient, F is the running cost of an alternating current and direct current system containing flexible direct current transmission;
and delta x is the variable quantity of the reactive compensation switching group number:
Δx＝|C-C0|
wherein C represents the column vector of the reactive compensation switching group number of the current operation mode, and C0And switching the column vectors of the group number for the previous operation mode.
Furthermore, the determination of the control variables in the objective function must satisfy the load flow equation of the system, and all variables should also satisfy respective upper and lower limit inequality constraints.
Further, the optimization model is solved by applying a genetic algorithm, wherein in the solving:
taking the sum of the network loss cost and the reactive power regulating quantity cost which is an optimal target function to be achieved as a fitness function of the genetic algorithm;
each combination of the control variables is an individual, load flow calculation is carried out on a system corresponding to each individual, if the load flow result is converged, the corresponding individual is a feasible solution, and the feasible solution is further substituted into a target function to calculate a fitness function value of the objective function; if not, discarding the individual;
all the converged individuals form a population, the individuals with lower fitness function values are selected from the population as parents, crossover and variation operations in the genetic algorithm sense are carried out to obtain offspring populations, whether the offspring populations meet convergence limiting conditions or not is judged, and if not, the iteration process is repeated until the offspring populations meet the convergence limiting conditions.
Furthermore, in order to meet the requirements of an actual optimization model and improve the solving efficiency, the invention improves the encoding mode, the mutation operator and the constraint condition processing method of the traditional genetic algorithm:
1) the selection of the coding mode adopts an integer and real number mixed coding mode, the reactive compensation switching group number adopts an integer coding mode, the active and reactive power injected into the alternating current system by the flexible direct current transmission system adopts a real number coding mode, and the coding of control variables is as follows:
X＝[C|Pref|Qref]＝[C1,C2,...,Cp|P1ref,P2ref,...,Pqref|Q1ref,Q2ref,...,Qqref]
in the formula, C represents the reactive compensation switching group number, PrefAnd QrefRespectively showing active power and reactive power injected into the alternating current system by the current converter, wherein p and q are the reactive compensation node number and the current converter node number correspondingly.
According to the reactive compensation switching group number, the susceptance value B of the compensation capacitor can be deducedi：
Figure BDA0001815365580000031
In the formula (I), the compound is shown in the specification,
Figure BDA0001815365580000032
and switching step length for reactive compensation.
2) In reactive power optimization, the converter injects active power P of the AC systemrefReactive power QrefThe number C of the reactive compensation switching groups is a self-constrained control variable, and the rated apparent power of the converter is constrained
Figure BDA0001815365580000033
Needs to be added to the objective function f as a penalty termQIn the step (2), a corrected solving objective function F is obtainedQ：
Figure BDA0001815365580000034
Figure BDA0001815365580000035
In the formula, lambda is a penalty factor, SiTo iteratively solve for the apparent power of each converter in the middle, sat (x) is a saturation function.
Figure BDA0001815365580000036
λ＝gen·λ0
In the formula, λ0Is the initial value of penalty factor, gen is evolution algebra, i.e. iteration algebra, Simin、SimaxThe minimum value and the maximum value of the rated apparent power of the converter are obtained.
The objective function is used as a fitness evaluation function for evaluating the quality of the solution, when the penalty factor linearly increases along with the iteration number, any infeasible solution exceeding the boundary is gradually eliminated by the competition process of the genetic algorithm, and the impracticable solution is eliminated by the competition process of the genetic algorithmSatisfying a penalty term
Figure BDA0001815365580000041
A feasible solution of (a) is left.
3) In the solution, an improved mutation algorithm is adopted, the mutation rate is gradually reduced along with the increase of an evolution algebra, and the preset minimum mutation rate is not exceeded:
Figure BDA0001815365580000042
in the formula, PmIs the rate of variation, Pm0Is an initial value of the rate of variation, PmstepIs the step size of the reduction of the variation rate, t is the iterative evolution algebra, t0Is a predetermined number of turning algebra, PmminIs a predetermined minimum variation rate.
The application also discloses island electric network operation optimizing system based on flexible direct current of multiterminal includes:
the optimization model establishing unit is used for establishing a multi-terminal flexible direct current-based alternating current power grid voltage optimization model, and an objective function of the optimization model comprises the economic cost required by reactive power regulation besides the traditional network loss index;
the variable determining unit is used for taking active power and reactive power injected into the alternating current system by the converter as control variables, and optimizing the distribution of the active power among parallel alternating current and direct current lines by utilizing the active power adjusting capacity of the VSC so as to indirectly control reactive power flow; the reactive power adjusting capability of the VSC is used as a continuously adjustable reactive power compensation means to be put into operation, the switching times of the capacitor bank are optimized, meanwhile, the control variable also comprises the switching group number of the compensation capacitor, and the state variable is the output of the generator and the voltage of each node of the system;
the model solving unit is used for solving the optimization model by utilizing an improved genetic algorithm, considering that the compensation capacitance is discrete quantity and the flexible active and reactive power is continuous quantity, and an integer and real number mixed coding mode is adopted in the solving; introducing a penalty term into the objective function to process inequality constraint conditions; and a variation algorithm which gradually decreases along with the increase of evolution algebra and does not exceed a preset minimum variation rate is adopted.
Compared with the prior art, the invention has the beneficial effects that:
the invention provides an innovative method for improving the operation optimization of an island power grid by using a flexible direct-current transmission system. The method utilizes the active and reactive power adjusting capacity of the VSC, and takes the economic cost of reducing active power loss and capacitor bank switching during switching of the power grid operation mode as an optimization target. Aiming at the characteristics of the model, the invention improves the coding mode, the mutation operator and the constraint condition processing method of the traditional genetic algorithm, provides an improved genetic algorithm for solving, and carries out simulation analysis by depending on the real cases of the Zhoushan multi-terminal flexible direct-current transmission demonstration project. The simulation result verifies the effectiveness and the rationality of the method, and shows that the model has certain significance for the reactive power optimization of a flexible-straight system and has good application prospect.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application.
FIG. 1 is a schematic flow chart of the overall concept of an embodiment of the present application;
FIG. 2 is a schematic diagram of an embodiment of the present application using a genetic algorithm to solve.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The establishment of the optimization model mainly comprises three parts of selecting control variables, constructing an objective function and determining constraint conditions. Wherein, the control variable is a group of variables which can be adjusted and controlled in the model; the objective function is a quantitative description of the optimization objective; the constraints are a series of equalities or inequalities that reflect the constraints imposed on the control variables in the model. Based on different control variable values, the operation mode of the system can be uniquely determined by combining known data such as system power generation, load, network structure and the like. The objective function value varies for different modes of operation, and generally, the optimization objective is to minimize or maximize the objective function value. The process of solving the optimization model is a process of solving the optimal objective function value by means of a solving algorithm, and the combination of the values of the control variables is ensured to be in the range of the constraint condition. Compared with the system before optimization, the optimized system has better performance on optimization indexes (such as network loss, economic cost of operation and the like).
As shown in fig. 1, in the present application, an optimization model is firstly established, a control variable is selected, an objective function is constructed, a constraint condition is determined, then the model is solved, an improved genetic algorithm is adopted for solving, simulation analysis and evaluation are performed on a solution result, and finally the benefit of the model is comprehensively evaluated.
The invention combines the practical difficulties of the island power grid in operation, such as larger power transmission and charging of a long-distance submarine cable, relative weakness of a grid structure, limited adjustment margin, obvious load peak valley, larger change of operation characteristics and the like. The establishment of the model mainly comprises three parts of selecting control variables, constructing an objective function and determining constraint conditions: introducing active and reactive control capability of multi-end flexible direct current into alternating current power grid voltage optimization, selecting active and reactive power injected into an alternating current system by a current converter as control variables, and simultaneously, the control variables also comprise switching groups of compensation capacitors and the like; considering the adjustment economic cost brought by operations such as generator terminal voltage adjustment, reactive compensation equipment switching during power grid operation mode switching and the like, integrating the network loss cost and the adjustment cost according to a certain coefficient ratio, and constructing a target function by taking the minimum total cost as a target; in the model, the determination of each control variable must satisfy a system power flow equation, the internal part of the direct current system needs to satisfy the constraint of power balance, the variables such as the active and reactive reference values of the current converter, the voltage of each node and the like also need to be within the upper and lower limit ranges, the above limiting factors are integrated, and the constraint condition is given.
Compared with the traditional direct current, one of the remarkable marks of the flexible direct current is that a Voltage Source Converter (VSC) based on a controllable turn-off device is adopted, so that the flexible direct current has the capability of actively adjusting active power and reactive power, and a new thought is provided for the voltage optimization control of an alternating current power grid. Based on the characteristic, the invention provides a model which optimizes the voltage of a power grid by using flexible direct current and takes the effects of reducing the active power grid loss and optimizing the switching times of a capacitor bank into consideration.
VSC-based active control characteristics: compared with a thyristor-based power grid commutation converter (LCC) in the traditional high-voltage direct current, the VSC has the fundamental advantage that the used devices are bidirectionally controllable and reflect on the output voltage U, so that the output voltage amplitude U and the phase delta are both controllable. Thus, from an ac system perspective, the VSC can be equivalent to a non-moment of inertia motor or generator, allowing almost instantaneous independent control of active and reactive power in the four quadrants of the PQ plane.
Reactive power optimization is a multivariable multi-constraint nonlinear programming problem, and variables of the multivariable multi-constraint nonlinear programming problem are divided into two types, namely control variables and state variables. Active and reactive control capability of multi-end flexible direct current is introduced into alternating current power grid voltage optimization, which is equivalent to active power P capable of injecting a converter into an alternating current systemrefAnd reactive power QrefAre considered to be control variables. On the basis of researching the characteristic, the invention provides an alternating current power grid voltage optimization model based on multi-terminal flexible direct current.
Operating optimization strategy with VSC: under normal operation, the dc system and the ac line connected in parallel share the transportation task. In AC/DC parallel systems, between AC and DC linesOften lack sufficient basis for the allocation of the transmission power. In scheduling operation, power allocation is often performed only according to operation experience, so that the benefit and the brought consequence of an allocation scheme are difficult to predict. In practice, the transmission power of the flexible dc power transmission system can be actively controlled, i.e. as long as the active power control command value P is determinedrefAnd the power distribution among the AC/DC parallel lines can be determined. If P of the converterrefIf the setting is too low, the advantages of low loss of a direct current line, controllable transmission power and the like of the flexible direct current transmission technology cannot be fully utilized; on the contrary, if P of the inverterrefThe situation that the transmission power flow directions of the alternating current and direct current parallel lines are opposite (called parallel circulating current) can occur due to the fact that the arrangement is too high, normal power dispatching is not facilitated, and the probability of faults of direct current equipment is increased to a certain extent. The invention takes the capability of the VSC converter for actively regulating the active power as the active control quantity for PrefAnd carrying out reasonable setting. The purpose of indirectly controlling reactive power flow is achieved by distributing the flow of active power between the parallel AC and DC lines.
The VSC has the reactive power regulation capability, and the VSC has similarity with reactive power compensation capacitors, SVCs and other devices in function. The island power grid has obvious reactive load change and variable operation modes, and equipment such as a reactive compensation capacitor and the like needs to be switched more frequently. Each operation of the equipment increases the damage degree, affects the service life and equivalently increases the investment and the operation and maintenance cost. That is, performing reactive power optimized scheduling requires some economic cost. If the reactive power adjusting capability of the VSC converter is utilized, the VSC converter is used as a reactive power compensation means capable of being continuously adjusted to be put into operation, and function complementation with the traditional reactive power compensation equipment can be achieved to a certain extent. On one hand, the switching times of the capacitor bank can be optimized, and the switching cost is reduced; on the other hand, the selection of voltage control means can be expanded, the flexibility of voltage control is improved, and the voltage level is further improved.
In conclusion, the characteristic that the VSC independently controls active power and reactive power provides feasibility for operation optimization of the island power grid, and pressure of frequent switching of reactive power compensation equipment can be relieved. It is feasible to optimize the operation mode of the island power grid by using flexible direct current.
With respect to the objective function: as mentioned above, the adjustment of the reactive power optimized schedule requires additional costs. Therefore, when considering the reactive power optimization of the power grid, in addition to the optimization goal of minimizing the grid loss, the cost equivalent to frequent switching operations of the transformer and the reactive power compensation equipment needs to be further considered. In addition, the flexible direct current transmission line and the parallel alternating current lines share certain active power transmission tasks, and if active power transmission of the direct current system is changed, active power transmission of the parallel alternating current lines is also changed. Therefore, when the active power of the dc system is adjusted, the influence of various aspects needs to be comprehensively considered.
In summary, for the ac/dc parallel operation system including the flexible dc power transmission, the reactive power optimization objective function combining the minimum operation cost and considering the adjustment cost can be represented by the following formula:
minF＝ατ(ΔPac+ΔPdc)+hcΔx (1)
in the formula (1), alpha is the price of electric energy, tau is the optimized time length, and delta PacFor ac active losses, Δ PdcFor direct current active losses, hcIn order to adjust the cost coefficient, F is the running cost of the alternating current-direct current parallel running system containing the flexible direct current transmission.
And delta x is the variable quantity of the reactive compensation switching group number:
Δx＝|C-C0| (2)
wherein C represents the column vector of the reactive compensation switching group number of the current operation mode, and C0And switching the column vectors of the group number for the previous operation mode.
Active and reactive control capability of multi-end flexible direct current is introduced into alternating current power grid voltage optimization, and active power and reactive power injected into an alternating current system by a current converter can be regarded as control variables. Meanwhile, the control variables also comprise the switching group number of the compensation capacitor and the like. The state variables are the output of the generator, the voltage of each node of the system and the like.
Variables and their constraints in the above objective function: in the optimization process, the control variables are determined to meet the load flow equation (3) of the system, and all the variables also meet the upper and lower limit inequality constraints of the variables.
Figure BDA0001815365580000081
Where n is the number of system nodes, PGiAnd QGiActive and reactive power output, P, for system generator nodesaciAnd QaciLoad active and reactive power, P, for node iirefAnd QirefActive and reactive power of the nodes are accessed for the direct current system. When the converter absorbs reactive power from the node, QirefTaking a positive value; when the converter outputs reactive power to the AC system, QirefTaking a negative value. QciFor the reactive compensation capacity of node i, GijAnd BijConductance and susceptance, V, of the elements in i rows and j columns of the node admittance matrixiAnd VjIs the voltage of node i, j, VijAnd thetaijIs the voltage amplitude and phase angle, V, between nodes i, ji、VjVoltages of nodes i, j, Q, respectivelyaciIs the load reactive power of node i.
In a multi-terminal flexible direct-current transmission system, active power transmitted by each converter should meet a balance relation. For m (m is more than or equal to 3) converters of the system, the following conditions are met:
Figure BDA0001815365580000082
inequality constraints on the control variables:
Figure BDA0001815365580000083
in the formula, SirefRated apparent power of inverter at node i, i.e. when
Figure BDA0001815365580000084
The converter itself is allowed to operate. CmaxAnd CminAnd switching the upper limit and the lower limit of the group number for reactive compensation.
Inequality constraints for state variables:
Figure BDA0001815365580000085
in the formula, Vimax、ViminIs the upper and lower limits of the node voltage, PGimax、QGimaxAnd P isGimin、QGiminRespectively the upper and lower limits of the output of the generator.
The solving method of the model comprises the following steps: the optimization model is non-linear and multi-extreme and contains discrete variables. Aiming at the model, the invention improves the coding mode, the mutation operator and the constraint condition processing method of the traditional genetic algorithm, and provides an improved genetic algorithm for solving.
The general steps of the genetic algorithm are shown in figure 2. In the solution, an objective function which is expected to reach the optimal value (the minimum value), namely the sum of the network loss cost and the reactive power regulating quantity cost, is used as a fitness function of the genetic algorithm. Each combination of control variables is an individual. Carrying out load flow calculation on a system corresponding to each individual, and if the load flow result is converged, indicating that the corresponding individual is a feasible solution, and substituting the feasible solution into a target function to calculate a fitness function value of the objective function; if not, the individual is discarded. All converged individuals constitute the starting population. And selecting individuals with lower fitness function values from the population as parents, and carrying out 'cross' and 'variation' operations in the genetic algorithm sense to obtain offspring populations. Judging whether the offspring population meets the convergence limiting condition, if so, finishing the calculation; and if not, taking the child population with the trend convergence as the initial population of the next iteration, and repeating the iteration process.
And (3) an encoding mode: the selection of a proper coding mode is a precondition for solving reactive power optimization by adopting a genetic algorithm. It directly affects the adaptability, speed and accuracy of the algorithm. The number of compensation switching groups is discrete quantity, and the flexible active power and the reactive power are continuous quantity. Therefore, an integer and real number mixed coding mode is adopted, the switching group number of the randomly selected compensation equipment is coded by an integer, and the flexible real number and the reactive real number are coded. The encoding of the control variables may be as follows:
Figure BDA0001815365580000091
in the formula, C represents the reactive compensation switching group number, PrefAnd QrefRespectively showing active power and reactive power injected into the alternating current system by the current converter, wherein p and q are the reactive compensation node number and the current converter node number correspondingly.
According to the number of the capacitor input groups, the susceptance value B of the compensation capacitor can be deducedi：
Figure BDA0001815365580000092
In the formula (I), the compound is shown in the specification,
Figure BDA0001815365580000093
and inputting step length for reactive compensation.
And (3) processing the constraint conditions: in most non-linear optimization problems, inequality constraints are usually added as penalty terms to the objective function to form an extended objective function. Since the genetic algorithm is an unconstrained optimization algorithm, it is very suitable to process the constraints by the penalty term. In reactive power optimization, the converter injects active power P of the AC systemrefReactive power QrefThe number C of the reactive compensation switching groups is a self-constrained control variable, and the rated apparent power of the converter is constrained
Figure BDA0001815365580000094
Needs to be added to the objective function f as a penalty termQIn the step (2), a corrected solving objective function F is obtainedQ。
Figure BDA0001815365580000095
Figure BDA0001815365580000096
In the formula, lambda is a penalty factor, SiTo iteratively solve for the apparent power of each converter in the middle, sat (x) is a saturation function.
Figure BDA0001815365580000101
λ＝gen·λ0 (12)
In the formula, λ0For the initial value of penalty factor, gen is evolution algebra (iteration algebra), Simin、SimaxThe minimum value and the maximum value of the rated apparent power of the converter are obtained.
In the algorithm, an objective function is used as a fitness evaluation function for evaluating the quality of a solution. When the penalty factor linearly increases along with the iteration number, any infeasible solution exceeding the boundary is gradually eliminated by the competition process of the genetic algorithm, and the penalty term is met
Figure BDA0001815365580000102
A feasible solution of (a) is left. The penalty factor value is initially small, considering that some solutions are not feasible in the initial solution population, but may imply part of the genes for the optimized solutions, or it is not far from the feasible optimized solutions, and if a severe penalty is applied immediately, a barrier effect is produced, which may result in loss of useful information in the population. The gradually increasing penalty factors not only avoid the loss of effective information, but also induce the search to jump away from the infeasible solution space, thereby achieving the purpose of optimizing.
Improvement of genetic operators: the mutation operator plays an important role in maintaining the diversity of the population and inhibiting precocity. When the variation rate is too large, random search is easily caused, and when the variation rate is too small, the ability of generating new individuals and inhibiting prematurity is weakened. The method adopts a mutation algorithm which gradually decreases along with the increase of evolution algebra and does not exceed a preset minimum mutation rate.
Figure BDA0001815365580000103
In the formula, PmIs the rate of variation, Pm0Is an initial value of the rate of variation, PmstepIs the step size of the reduction of the variation rate, t is the iterative evolution algebra, t0Is a predetermined number of turning algebra, PmminIs a predetermined minimum variation rate.
The genetic algorithm is an iterative algorithm, and the mutation operation is an operation of changing a control variable for obtaining a better objective function in each iteration of the genetic algorithm. The mutation operator is improved, and the main purpose is to improve the model solving efficiency.
In fact, for most discrete control variables, the variation is adjusted step by step on the original basis, as the variation progresses, the population tends to be stable, and the variables should be changed within a smaller range of the current value as much as possible to improve the variation efficiency. Thus, at the beginning of evolution, the mutation rate PmThe population variation is large, the population variation is severe, and the difference in the population is increased, so that the probability of covering the optimal solution is improved as much as possible, and the premature phenomenon is inhibited. As the evolution progresses, the population gradually converges to the vicinity of the optimal solution, and the mutation rate PmAnd decreases. At the end of evolution, PmIs maintained at a lower level to avoid the individual control variables from oscillating around the optimal solution, which could falsely determine the process as a non-convergence situation.
In order to make the technical solutions of the present application more clearly understood by those skilled in the art, the technical solutions of the present application will be described in detail below with reference to specific examples and comparative examples.
The Zhoushan power grid is a typical island power grid and is limited by geographical conditions, the north island grid is weak, each island is weakly connected with the island and the power grid among the islands, no large power supply is supported, and the power supply reliability and the operation flexibility are low. The Zhoushan multi-terminal flexible direct-current transmission demonstration project is the first five-terminal flexible direct-current transmission project in the world, and is formally put into use in 2014, and the direct-current voltage grade is 200 kV. However, in practical use, the ac and dc systems are basically in independent operation states, which limits the VSC voltage regulating function.
Taking a navian power grid comprising a five-terminal flexible direct-current transmission system as an example, matlab is used for compiling an optimization program, and an improved genetic algorithm is used for analyzing a typical example of the navian power grid.
Basic parameters: the total number of nodes of the Zhoushan power grid 110kV and above is 106, wherein the number of compensation capacitor nodes is 54, and the number of converter station nodes is 5.
In this example, the VSC is controlled by constant active power and constant reactive power. The number of the control variables is 59, and the control variables comprise 49 switching quantities (discrete control variables) of alternating current node compensation devices, 5 reactive quantities of converter nodes and 5 active quantities (continuous control variables) of the converter nodes. The compensation node voltage ranges from 0.9 to 1.1 (per-unit value). Each transformer substation is limited by the construction scale, and the reactive power regulation range is restricted by upper and lower limits. And the reactive configuration of each group of transformers of the 500 kV transformer substation is considered according to 2 groups of capacitors (2 multiplied by 60Mvar) and 2 groups of reactors (2 multiplied by 60 Mvar). Each transformer of the 220 KV substation is provided with 6 groups of capacitors (6 multiplied by 10Mvar) and 3 groups of reactors (3 multiplied by 10 Mvar); each group of transformers of the 110kv substation is provided with 6 groups of capacitors (6 × 4.8 Mvar).
And selecting three sequential transition operation modes of peak load, waist load and valley load for discussion on the selection of the load parameters. Wherein the load at waist load is 60% of peak load, and the load at valley load is 45% of peak load.
In the aspect of adjusting economic cost, the maximum annual switching times of each group of parallel capacitors are considered to be about 1000 times and the service life is about 20 years by referring to the current capacitor switching condition of the Zhoushan power grid. In terms of the loss cost of the network, the electricity price is considered to be about 0.5 yuan/kilowatt hour, and the equivalent average running time of each line is calculated to be 1000 hours between two adjustments.
The improved genetic algorithm parameter population size NIND is 300, and the maximum genetic algebra MAXGEN is 1000.
And (4) analyzing results: when the flexible power is taken as a reactive power source and participates in voltage optimization in a reactive power control mode, corresponding peak-waist mode conversion and waist-valley mode conversion are carried out, and the reactive power optimization result and the total economic cost are shown in tables 1 and 2.
TABLE 1 VSC participated in reactive power optimization result of voltage optimization in front and rear peak-waist mode
Figure BDA0001815365580000111
Figure BDA0001815365580000121
TABLE 2 reactive power optimization results of waist-valley mode before and after VSC participated in voltage optimization
Figure BDA0001815365580000122
The active power adjusting capability of the flexible direct current is also used as an active control quantity, the distribution of active power among parallel alternating current and direct current lines is adjusted, then reactive power flow is indirectly controlled, the optimization searching range can be effectively expanded, and the optimization effect is improved. At this time, the reactive power optimization results and the total economic cost corresponding to the peak-to-waist mode transition and the waist-to-valley mode transition are shown in tables 3 and 4.
TABLE 3 reactive power optimization result of VSC participating in voltage optimization in front and rear peak-waist mode
Figure BDA0001815365580000123
Table 4 reactive power optimization results of waist-valley mode before and after VSC participates in voltage optimization
Figure BDA0001815365580000124
Figure BDA0001815365580000131
From the results in the table, if softness and straightness only take part in voltage optimization as a reactive power source, the total economic cost is 314.84 ten thousand yuan and 192.33 ten thousand yuan corresponding to peak-to-waist mode transition and waist-to-valley mode transition, which are respectively reduced by 12.18% and 18.76% compared with the situation that softness and straightness do not take part in optimization. If the active adjustment capacity of the gentle and straight line is also used as the active control amount, the corresponding total economic cost is 302.58 ten thousand yuan and 174.38 thousand yuan respectively, which are respectively reduced by 15.61% and 26.34%, compared with the situation that the gentle and straight line is only used as a reactive power source to participate in optimization, the corresponding total economic cost is respectively reduced by 3.89% and 9.33%, and compared with the situation that the gentle and straight line does not participate in optimization, the corresponding total economic cost is respectively reduced by 15.61% and 26.34%. Therefore, after the flexible regulation capacity in both active and reactive power is fully utilized, the total economic cost is greatly reduced compared with the traditional voltage optimization.
According to the invention, through example simulation, the active and reactive independent control characteristics of the flexible direct current system can be applied to the voltage operation optimization of the island power grid, and the optimization model has the feasibility in engineering. The pressure of frequent switching of reactive compensation equipment is relieved, the flexibility of voltage control is improved, and meanwhile, the system can make a contribution to the operation economy and the voltage stability of a regional power grid on the whole.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims (8)

1. The island power grid operation optimization method based on the multi-end flexible direct current is characterized by comprising the following steps of:
establishing an alternating current power grid voltage optimization model based on multi-terminal flexible direct current, wherein an objective function of the optimization model comprises the economic cost required by reactive power regulation besides the traditional network loss index;
the active power and the reactive power injected into the alternating current system by the converter are taken as control variables, the active capacity is actively adjusted by the VSC, and the distribution of the active power among the parallel alternating current and direct current lines is optimized, so that the reactive power flow is indirectly controlled; the method comprises the following steps that the reactive power adjusting capability of the VSC is used as a reactive power compensation means capable of being continuously adjusted to be put into operation, the switching times of a capacitor bank are optimized, meanwhile, control variables also comprise the switching group number of compensation capacitors, and state variables are the output of a generator and the voltage of each node of a system;
converter rated apparent power constraint in reactive power optimization
Figure FDA0003143462570000011
Needs to be added to the objective function f as a penalty termQIn the step (2), a corrected solving objective function F is obtainedQ：
Figure FDA0003143462570000012
Figure FDA0003143462570000013
In the formula, lambda is a penalty factor, SiIn order to iteratively solve the apparent power of each converter in the intermediate process, sat (x) is a saturation function;
Figure FDA0003143462570000014
λ＝gen·λ0
in the formula, λ0For the initial value of the penalty factor, gen is an evolutionary algebra, namely an iterative algebra;
an improved genetic algorithm is utilized to solve an optimization model, considering that compensation capacitance is discrete quantity and flexible and direct active and reactive power is continuous quantity, an integer and real number mixed coding mode is adopted in the solution, an integer coding mode is adopted for reactive power compensation switching group number, and a real number coding mode is adopted for active and reactive power injected into an alternating current system by a flexible direct current power transmission system; in reactive power optimization, the converter injects active power P of the AC systemrefReactive power QrefAnd the reactive compensation switching group number C is a self-constrained control variable, and a penalty term is introduced into the objective function to process inequality constraint conditions; adopting a variation algorithm which gradually decreases along with the increase of evolution algebra and does not exceed a preset minimum variation rate;
in the solution, an improved mutation algorithm is adopted, the mutation rate is gradually reduced along with the increase of an evolution algebra, and the preset minimum mutation rate is not exceeded:
Figure FDA0003143462570000015
in the formula, PmIs the rate of variation, Pm0Is an initial value of the rate of variation, PmstepIs the step size of the reduction of the variation rate, t is the iterative evolution algebra, t0Is a predetermined number of turning algebra, PmminIs a predetermined minimum variation rate.
2. The method for optimizing the operation of the island power grid based on the multi-terminal flexible direct current of claim 1, wherein the objective function in the optimization model is represented by the following formula:
min F＝ατ(ΔPac+ΔPdc)+hcΔx
in the formula, alpha is the price of electric energy, tau is the optimized time length, delta PacFor ac active losses, Δ PdcFor direct current active losses, hcIn order to adjust the cost coefficient, F is the running cost of an alternating current and direct current system containing flexible direct current transmission;
and delta x is the variable quantity of the reactive compensation switching group number:
Δx＝|C-C0|
wherein C represents the column vector of the reactive compensation switching group number of the current operation mode, and C0And switching the column vectors of the group number for the previous operation mode.
3. The island grid operation optimization method based on multi-terminal flexible direct current of claim 2, wherein the control variables in the objective function must be determined to satisfy the tidal current equation of the system, and all variables should satisfy the inequality constraints of their respective upper and lower limits.
4. The island grid operation optimization method based on multi-terminal flexible direct current of claim 1, wherein the optimization model is solved by applying a genetic algorithm, and in the solving:
taking the sum of the network loss cost and the reactive power regulating quantity cost which is an optimal target function to be achieved as a fitness function of the genetic algorithm;
each combination of the control variables is an individual, load flow calculation is carried out on a system corresponding to each individual, if the load flow result is converged, the corresponding individual is a feasible solution, and the feasible solution is further substituted into a target function to calculate a fitness function value of the objective function; if not, discarding the individual;
all the converged individuals form a population, the individuals with lower fitness function values are selected from the population as parents, crossover and variation operations in the genetic algorithm sense are carried out to obtain offspring populations, whether the offspring populations meet convergence limiting conditions or not is judged, and if not, the iteration process is repeated until the offspring populations meet the convergence limiting conditions.
5. The island grid operation optimization method based on multi-terminal flexible direct current of claim 4, wherein the coding mode, mutation operator and constraint condition processing method of the traditional genetic algorithm are improved:
the selection of the coding mode adopts an integer and real number mixed coding mode, and the coding of the control variable is as follows:
X＝[C|Pref|Qref]＝
[C1,C2,...,Cp|P1ref,P2ref,...,Pqref|Q1ref,Q2ref,...,Qqref]
in the formula, C represents the reactive compensation switching group number, PrefAnd QrefRespectively representing active power and reactive power injected into the AC system by the converter, and the corresponding p and q are the number of reactive compensation nodes and conversionNumber of flow device nodes.
6. The method for optimizing the operation of the island grid based on the multi-terminal flexible direct current of claim 5, wherein the susceptance value B of the compensation capacitor can be derived according to the number of reactive compensation switching groupsi：
Figure FDA0003143462570000031
In the formula (I), the compound is shown in the specification,
Figure FDA0003143462570000032
and switching step length for reactive compensation.
7. The island grid operation optimization method based on multi-terminal flexible direct current of claim 1, wherein the objective function is used as a fitness evaluation function for evaluating the quality of the solution, when the penalty factor increases linearly with the number of iterations, any infeasible solution exceeding the boundary will be gradually eliminated by the competition process of the genetic algorithm, and the penalty term is satisfied
Figure FDA0003143462570000033
A feasible solution of (a) is left.
8. Island electric wire netting operation optimization system based on flexible direct current of multiterminal, characterized by includes:
the optimization model establishing unit is used for establishing a multi-terminal flexible direct current-based alternating current power grid voltage optimization model, and an objective function of the optimization model comprises the economic cost required by reactive power regulation besides the traditional network loss index;
the variable determining unit is used for taking active power and reactive power injected into the alternating current system by the converter as control variables, and optimizing the distribution of the active power among parallel alternating current and direct current lines by utilizing the active power adjusting capacity of the VSC so as to indirectly control reactive power flow; the reactive power adjusting capability of the VSC is used as a continuously adjustable reactive power compensation means to be put into operationOptimizing the switching times of the capacitor bank, meanwhile, controlling variables also comprise the switching group number of the compensation capacitor, and state variables are the output of the generator and the voltage of each node of the system; converter rated apparent power constraint in reactive power optimization
Figure FDA0003143462570000034
Needs to be added to the objective function f as a penalty termQIn the step (2), a corrected solving objective function F is obtainedQ：
Figure FDA0003143462570000035
Figure FDA0003143462570000036
In the formula, lambda is a penalty factor, SiIn order to iteratively solve the apparent power of each converter in the intermediate process, sat (x) is a saturation function;
Figure FDA0003143462570000037
λ＝gen·λ0
in the formula, λ0For the initial value of the penalty factor, gen is an evolutionary algebra, namely an iterative algebra;
the model solving unit is used for solving an optimization model by utilizing an improved genetic algorithm, considering that compensation capacitance is discrete quantity and flexible and direct active and reactive power is continuous quantity, an integer and real number mixed coding mode is adopted in the solving, an integer coding mode is adopted for reactive power compensation switching group number, and a real number coding mode is adopted for active and reactive power injected into an alternating current system by the flexible direct current power transmission system; in reactive power optimization, the converter injects active power P of the AC systemrefReactive power QrefAnd the reactive compensation switching group number C is a self-constrained control variable, and a penalty term is introduced into the objective function to process inequality constraint conditions; by evolution with timeA variation algorithm of which the algebraic increase is gradually reduced and does not exceed a preset minimum variation rate;
in the solution, an improved mutation algorithm is adopted, the mutation rate is gradually reduced along with the increase of an evolution algebra, and the preset minimum mutation rate is not exceeded:
Figure FDA0003143462570000041
in the formula, PmIs the rate of variation, Pm0Is an initial value of the rate of variation, PmstepIs the step size of the reduction of the variation rate, t is the iterative evolution algebra, t0Is a predetermined number of turning algebra, PmminIs a predetermined minimum variation rate.
CN201811139182.5A 2018-09-28 2018-09-28 Island power grid operation optimization method and system based on multi-terminal flexible direct current Active CN109193634B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811139182.5A CN109193634B (en) 2018-09-28 2018-09-28 Island power grid operation optimization method and system based on multi-terminal flexible direct current

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811139182.5A CN109193634B (en) 2018-09-28 2018-09-28 Island power grid operation optimization method and system based on multi-terminal flexible direct current

Publications (2)

Publication Number Publication Date
CN109193634A CN109193634A (en) 2019-01-11
CN109193634B true CN109193634B (en) 2021-08-24

Family

ID=64906684

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811139182.5A Active CN109193634B (en) 2018-09-28 2018-09-28 Island power grid operation optimization method and system based on multi-terminal flexible direct current

Country Status (1)

Country Link
CN (1) CN109193634B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110053521B (en) * 2019-03-08 2020-11-03 北京交通大学 Urban rail transit traction power supply system and vehicle-network coordination parameter optimization method
CN113659578B (en) * 2021-06-29 2023-10-24 国网浙江省电力有限公司嘉兴供电公司 UPFC and STATCOM optimal configuration method considering available power transmission capacity of system

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103441510B (en) * 2013-08-05 2016-01-20 南方电网科学研究院有限责任公司 Regional power grid reactive power optimization method comprising flexible direct current transmission system
CN106340887B (en) * 2016-09-18 2019-01-22 国网福建省电力有限公司 Meter and the AC/DC Power System idle work optimization method of state of a control conversion
CN106655207B (en) * 2017-03-21 2020-03-27 国网山东省电力公司枣庄供电公司 Power distribution network reactive power optimization system and method based on multi-data analysis
CN107681670A (en) * 2017-06-22 2018-02-09 国家电网公司 Power network distribution idle work optimization method
CN107516892B (en) * 2017-07-21 2020-09-29 重庆邮电大学 Method for improving electric energy quality based on processing active power optimization constraint conditions

Also Published As

Publication number Publication date
CN109193634A (en) 2019-01-11

Similar Documents

Publication Publication Date Title
CN110690732B (en) Photovoltaic reactive power partition pricing power distribution network reactive power optimization method
García-Triviño et al. Power control based on particle swarm optimization of grid-connected inverter for hybrid renewable energy system
CN109842123B (en) Phase modulator and layered structure ultra-high voltage direct current receiving end converter station coordinated dynamic reactive power optimization method
CN109066694B (en) multi-objective power flow optimization method for power system containing inter-line power flow controller
CN106410817B (en) A kind of tractive power supply system negative phase-sequence is idle and voltage fluctuation complex optimum compensation method
Hu et al. Coordinated active and reactive power control for distribution networks with high penetrations of photovoltaic systems
CN114362267B (en) Distributed coordination optimization method for AC/DC hybrid power distribution network considering multi-objective optimization
CN114928057B (en) Multi-target power flow optimization method suitable for new energy multi-terminal flexible direct system
CN109193634B (en) Island power grid operation optimization method and system based on multi-terminal flexible direct current
CN111614110A (en) Receiving-end power grid energy storage optimization configuration method based on improved multi-target particle swarm optimization
CN113629715B (en) UPFC multi-objective optimization configuration method considering power transmission capability
Skaria et al. Genetic algorithm based optimal location of SVC in power system for voltage stability enhancement
An et al. Enhance transient voltage stability by retrofitting thermal power unit to synchronous condenser
CN206060207U (en) A kind of Reactive Power Control device based on intelligent transformer
CN114899829A (en) Voltage stability assessment method and device
CN115588991A (en) Method for establishing optimal power flow model of three-phase unbalanced distribution network
CN112909958A (en) Voltage regulation method for power distribution network
Ataeizadeh et al. Optimal location of flexible alternating current transmission systems devices to achieve multiple goals in power systems
Ji et al. Operation and optimization technologies of active distribution network with multi-terminal soft open points
Thirugnanam et al. Fuzzy based active and reactive power support to the distribution network using electric vehicles
CN106058883A (en) Reactive power control system based on intelligent transformer and control method thereof
Yuquan et al. Research on transmission and distribution network reactive power/voltage coordinated control technology of large receiving-end urban power grid
CN113162060B (en) Opportunity constraint optimization-based active power distribution network two-stage reactive power regulation method
CN112994027B (en) Method and device for configuring energy storage capacity under secondary reactive voltage control strategy
Ting et al. Implementation of dynamic reactive power coordination control strategy based on AVC-SCADA system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant