CN109274136A - A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization - Google Patents
A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization Download PDFInfo
- Publication number
- CN109274136A CN109274136A CN201811241127.7A CN201811241127A CN109274136A CN 109274136 A CN109274136 A CN 109274136A CN 201811241127 A CN201811241127 A CN 201811241127A CN 109274136 A CN109274136 A CN 109274136A
- Authority
- CN
- China
- Prior art keywords
- max
- value
- reactive power
- particle swarm
- voltage
- 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.)
- Pending
Links
- 239000002245 particle Substances 0.000 title claims abstract description 101
- 238000005457 optimization Methods 0.000 title claims abstract description 79
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000009826 distribution Methods 0.000 claims abstract description 28
- 238000013178 mathematical model Methods 0.000 claims abstract description 11
- 238000004422 calculation algorithm Methods 0.000 claims description 76
- 230000003044 adaptive effect Effects 0.000 claims description 14
- 230000008859 change Effects 0.000 claims description 9
- 230000006978 adaptation Effects 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000009466 transformation Effects 0.000 claims description 5
- 238000009827 uniform distribution Methods 0.000 claims description 4
- 230000005611 electricity Effects 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 32
- 238000010248 power generation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 230000008901 benefit Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 3
- 230000002776 aggregation Effects 0.000 description 2
- 238000004220 aggregation Methods 0.000 description 2
- 230000001580 bacterial effect Effects 0.000 description 2
- 239000003990 capacitor Substances 0.000 description 2
- 230000002431 foraging effect Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 241000271566 Aves Species 0.000 description 1
- 241000544061 Cuculus canorus Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008602 contraction Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000011089 mechanical engineering Methods 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000005610 quantum mechanics Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
- 230000005428 wave function Effects 0.000 description 1
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
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
-
- H02J3/383—
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
- Y02E10/56—Power conversion systems, e.g. maximum power point trackers
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The present invention relates to a kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization, belongs to photovoltaic plant Reactive-power control technical field.The method of the present invention includes the following steps: step 1 analyzes influence of the photovoltaic electric station grid connection to distribution network system voltage；Step 2 establishes the mathematical model of the optimization of distribution network var compensation containing photovoltaic plant；Step 3 solves the photovoltaic system idle work optimization model built using quantum particle swarm optimization.Photovoltaic system idle work optimization method provided by the invention based on quanta particle swarm optimization, can be effectively reduced active power loss, it will be apparent that the voltage value for improving each node runs distribution network system more economically steadily.
Description
Technical Field
The invention belongs to the technical field of reactive power optimization of photovoltaic power stations, and particularly relates to a reactive power optimization method of a photovoltaic system based on a quantum particle swarm algorithm.
Background
Since fossil energy has a limited reserve, is not renewable, and has high pollution in the conventional petrochemical energy, more and more researchers are engaged in the research of green pollution-free renewable energy. As the solar energy resource as renewable energy is widely distributed, inexhaustible and inexhaustible, photovoltaic power generation is receiving more and more attention and research. The power generation capacity of the photovoltaic power generation system is influenced by factors such as weather and seasons, so that the photovoltaic power generation is unstable. After photovoltaic power generation is incorporated into a power distribution network, the high permeability of the photovoltaic power generation can cause the voltage of the power distribution network to rise and the short-circuit current to increase, the tide distribution condition of the traditional power distribution network can be thoroughly changed, and great influence is generated on the network node voltage, the line transmission loss and the like, so that the control and management of a power distribution system become more complicated. At present, the problem of reactive power optimization of a photovoltaic system is closely concerned internationally and domestically. The aim of researching reactive power optimization of the power system is to ensure the balance of reactive power by adjusting the scientific and reasonable distribution of reactive power flow, effectively reduce the network loss and improve the economic benefit by prompting the safe and stable operation of the power system.
For a photovoltaic power station which is connected with a public power grid through a private line, the photovoltaic power station is provided with a reactive voltage control system, has certain reactive power, and provides reactive support for the power grid when the power grid fails or is abnormal, so that voltage collapse is prevented. When the reactive capacity of the photovoltaic inverter can not meet the voltage regulation requirement of the system, reactive compensation is carried out by reasonably configuring the reactive compensation device.
The reactive power optimization of the power system, namely, a reactive power regulation means for optimizing one or more performance indexes (minimum active network loss, minimum annual expenditure cost, optimal voltage quality and the like) of the system on the premise of meeting all specified constraint conditions by optimizing some control variables when the structural parameters and the load condition of the power system are given, belongs to the problem of multi-constraint nonlinear programming. At present, algorithms for reactive power optimization mainly fall into two categories: the first type is a classical reactive power optimization algorithm, such as a linear programming method, a mixed integer programming method, an analytic method and the like. The algorithms have high requirements on mathematical models, the accuracy and the real-time performance of the algorithms are difficult to meet at the same time, and the global optimal solution cannot be found to a great extent. The second type is reactive power optimization algorithm based on artificial intelligence, such as genetic algorithm, immune algorithm, tabu search, and the like. Compared with the traditional reactive power optimization algorithm, the algorithm has the advantages of being capable of randomly searching and better processing discrete and multi-objective optimization problems, but has inevitable defects. For example, a bacterial foraging algorithm is proposed in documents (maxiyuan, wu dazangwen, kuangliang, etc.. wind/light/storage hybrid microgrid power supply optimized configuration adopting an improved bacterial foraging algorithm [ J ]. china motor engineering, 2011,31(25):17-25.), but the precision is not high enough, and particularly, when a multimodal problem is optimized, all optimal solutions are difficult to find; the literature (Mohd Herwan summer, Zurici Mustaffa. Cuckoo search as an optizer for optimal reactive power distribution documents [ C ]// 20173 rd International Conference on Control, Automation and Robotics (ICCAR),2017: 735-; the PSO algorithm is inspired by bird predation behavior, and is a group intelligent Optimization algorithm proposed in 1995, which is currently applied to problems of power system independent control, optimal power flow calculation, unit combination and the like, but is easy to fall into a local extreme point and is not a global convergence algorithm; the speed and position evolution formula of the algorithm enables the randomness and intelligence of the particle swarm to be low; the dependence of the algorithm on the upper speed limit makes it less robust.
Through research, the human learning process is very similar to the quantum behavior of particles, and has great uncertainty. Therefore, a new group intelligent algorithm, namely a Quantum Particle Swarm Optimization (QPSO) algorithm, is proposed in documents (billow, Xuxu Xuehui, Schotti, etc.. STATCOM addressing and capacity Optimization [ J ]. China electro-mechanical engineering, 2015,35 (a supplement): 75-81.) based on an improved multi-group Quantum particle swarm algorithm, and the QPSO algorithm has been proved to have the characteristics of good global search performance, few control parameters, and the like.
At present, no method which is particularly effective in applying quantum particle swarm algorithm to reactive power optimization of a power distribution network comprising a photovoltaic power station exists in the prior art.
Disclosure of Invention
In view of the above, the invention aims to provide a photovoltaic system reactive power optimization method based on a quantum particle swarm algorithm, which performs reactive power optimization on a power distribution network containing a photovoltaic power station, so as to achieve the purposes of reducing active network loss, improving system node voltage, and enabling the power distribution network system to run more economically and stably.
In order to achieve the purpose, the invention provides the following technical scheme:
a photovoltaic system reactive power optimization method based on a quantum particle swarm algorithm comprises the following steps:
s1, analyzing the influence of grid connection of the photovoltaic power station on the voltage of the power distribution network system;
s2, establishing a reactive power optimization mathematical model of the distribution network containing the photovoltaic power station;
s3, solving the reactive power optimization mathematical model of the power distribution network of the photovoltaic power station by using a quantum particle swarm optimization algorithm.
Further, the S1 includes:
s11, obtaining a voltage drop expression, wherein the voltage drop at two ends of a line in the simple grid-connected circuit of the photovoltaic power station is as follows:
dU＝ΔU+jδU
wherein, suppose that the phase angle of the voltage of the line at the terminal is 0, delta is the phase angle difference of the voltage, P is the active power, Q is the reactive power, R is the resistance, X is the reactance, U1For the output voltage, P, of the photovoltaic power station1、Q1Respectively, the active power of the photovoltaic power station, the reactive power of the photovoltaic power station, U2For system grid-connected voltage, P2，Q2Respectively are grid-connected active power and grid-connected reactive power;
s12, simplifying a voltage drop expression, wherein the voltage impedance R of the power grid is larger than X, the voltage impedance R is in a resistance characteristic, the reactance X is ignored, and the voltage drop expression is as follows:
further, the reactive power optimization mathematical model in S2 includes an objective function, a power equation constraint and an inequality constraint.
Further, the expression of the objective function is
min F＝βτmaxΔPΣ+(α+γ)KCQCΣ
Wherein β is the price of electricity per degree, taumaxFor annual maximum load loss hours, α and gamma respectively represent annual depreciation maintenance rate and investment recovery rate of reactive compensation equipment, KCPrice per unit capacity of reactive power compensator, QCΣIs the sum of the reactive compensation capacities, Δ PΣThe network loss after reactive compensation.
Further, the power equation is constrained by the expression
Wherein i, j is a load node, N is the number of load nodes, GijIs the conductance between i, j, BijIs the susceptance between i, j, deltaijIs the phase angle difference between i, j.
Further, the inequality constraints comprise control variable constraints and state variable constraints, wherein the expression of the control variable constraints is
Ui.min≤Ui≤Ui.max
QC.min≤QC≤QCmax
Tt.min≤Tt≤Tt.max
The expression of the state variable constraint is
UD.min≤UD≤UD.max
Wherein, UiIs the system generator terminal voltage (i is 1,2.. G, G is the number of generators), Ui.max,Ui.minThe upper and lower limits of the generator terminal voltage of the system are set; qCFor the capacity of the reactive power compensation means, QC.max,QC.minThe upper limit and the lower limit of the capacity of the reactive power compensation device are set; t istFor ratio change of on-load tap-changing transformers, Tt.max,Tt.minThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; u shapeDIs the value of the node voltage, UD.max,UD.minThe upper and lower limits of the node voltage value.
Further, a penalty function method is adopted for processing the objective function and the state variable constraint, and at the moment, the reactive power optimization objective function is as follows:
wherein:
wherein, UDiIs the voltage value of the ith node, UDi.max,UDi.minThe voltage values of the ith node are upper and lower limits, lambda is a penalty coefficient, and N is the number of system nodes.
Further, the quantum particle swarm optimization algorithm in S3 includes:
s31, setting parameters including the population specifications of quantum particle swarm operationModulo nPop, maximum evolution algebra GmaxD, c, and D, wherein the contraction-expansion factor β is 0.75, the initial global optimum adaptation value is infinity, and the position x of the ith particlei＝(xi1,xi2..xiD)；
S32, evaluation Q (t)0) The fitness function value of (a) is evaluated by taking the objective function F (x) as a fitness function;
s33, record Q (t)0) Taking the medium optimal fitness function value as a local optimal fitness value and a corresponding optimal individual;
s34, comparing the local optimal adaptive value with the global optimal adaptive value, and updating and recording the global optimal value and the corresponding optimal individual;
s35, carrying out evolution, and updating a contraction-expansion coefficient β by adopting a change method that parameters of a quantum particle swarm algorithm linearly reduce along with evolution algebra change:
β＝(a-b)*(Gmax-t)/Gmax+b
wherein, a is 1, b is 0.5, and t is the current generation evolution algebra;
s36, updating the position, namely updating the position of the particle in the feasible space, wherein the position updating formula of the particle is as follows:
wherein L represents a weighted distance between the particle and the population average optimal position, u is a random number obeying uniform distribution over an interval [0,1], p is a local attraction potential of the particle, p represents a random position of each particle between the local optimal position and the global optimal position, and is determined by the following formula:
pij(t)＝φPij(t)+(1-φ)Pgj(t),j＝1,2...D
wherein, Pij(t) is the local optimum position of the t-th generation of particle i, Pgj(t) is the global optimum position of the t-th generation, and phi represents the interval [0,1]]The random number is uniformly distributed, and L represents the weighted distance between the particle and the average optimal position of the population:
L(t+1)＝2β·|m(t)-X(t)|
wherein m (t) is the average optimal position, defined as the average of the local optimal values of all particles in the population:
s37, taking the random number u uniformly distributed in the interval [0,1], and updating the position of the particle i:
if u is less than 0.5,
if u is greater than 0.5, then,
s38, calculating the adaptive value F (x) of the particles at the moment, and updating and recording the local optimal value, the global optimal value and the corresponding optimal individual;
s39, judging whether the operation of the quantum particle swarm optimization algorithm reaches the maximum evolution algebra GmaxIf the maximum evolution algebra G is not reachedmaxReturning to S35; if the maximum evolution algebra G is reachedmaxAnd the algorithm ends.
Further, the S3 specifically includes:
s41, reading in original data, wherein the original data comprises power system parameters, parameter value range and initial value of quantum particle swarm optimization, and randomly generating an initialization population Q (t)0) Randomly generated within the control variable constraintsTo an initial positionInitializing a global optimal variable;
s42, using the objective function F (x)0) And (3) evaluating a fitness function, sending the initial control variable into a Matpower toolkit of the MATLAB for load flow calculation to obtain the network loss, wherein the target function is as follows:
evaluating by taking an objective function F (x) as a fitness function;
s43, record Q (t)0) Taking the medium optimal fitness function value as a local optimal fitness value and a corresponding optimal individual;
s44, comparing the local optimal adaptive value with the global optimal adaptive value, and updating and recording the global optimal value and the corresponding optimal individual;
s45, updating the positions of the particle swarm according to the particle position updating formula:
calculating the network loss by Matpower according to the control variable value at the moment to obtain cost F (x);
s46, comparing the adaptive value with the local optimal position and the global optimal position of the particle swarm, and updating and recording the local optimal position, the global optimal position and the corresponding optimal individual at the moment;
s47, repeating S45 and S46 until reaching the maximum evolution generation GmaxThen, the optimum control variable value, the network loss and the cost F at the moment are recorded.
The invention has the beneficial effects that: the photovoltaic system reactive power optimization method based on the quantum particle swarm algorithm can obviously improve the node voltage value, reduce the active network loss and enable a power distribution network system to run more economically and stably.
Drawings
FIG. 1: the flow diagram of the method of the invention is shown.
FIG. 2: photovoltaic power plant simple circuit that is incorporated into power networks.
FIG. 3: photovoltaic grid-connected inverter schematic diagram.
FIG. 4: schematic diagram of basic idea of algorithm design.
FIG. 5: IEEE14 node system diagram.
FIG. 6: the node voltages before and after optimization are compared.
FIG. 7: QPSO and PSO algorithm simulation comparison graph.
Detailed Description
Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
The invention provides a photovoltaic system reactive power optimization method based on a quantum particle swarm algorithm, which specifically comprises the following steps as shown in fig. 1 and 4:
s1, analyzing the influence of the grid connection of the photovoltaic power station on the voltage of the power distribution network system, wherein the influence of the active power and the reactive power of the photovoltaic power station on the voltage of the power grid is included.
The voltage level of each node in the power grid is determined by the power flow distribution of the power grid, and when a photovoltaic power station is connected into the system, the power flow distribution is influenced, so that the voltage of each node changes. A simple circuit for grid connection of photovoltaic power plants is shown in fig. 2. In fig. 2, P is active power, Q is reactive power, R is resistance, X is reactance, and assuming that the phase angle of the voltage at the terminal of the line is 0, δ is the phase angle difference of the voltage, the voltage drop at the two ends of the line is:
dU＝ΔU+jδU (2)
according to typical transmission line impedance parameters, the voltage impedance of the power grid has R & gt X, and is in a resistance characteristic, and the reactance X can be ignored. At this time, the voltage drop expression is:
it can be seen that the lateral voltage drop component Δ U of the distribution network line is mainly affected by the active power, and the longitudinal voltage drop component δ U is mainly affected by the reactive power. In order to maximize the generated power, the photovoltaic power station generally does not limit the active power, so that the active power can be improved to reduce the voltage fluctuation on the transmission line while compensating the reactive power. At present, the most basic measures for reactive compensation and voltage regulation of photovoltaic power stations are parallel capacitors and reactors, and because the parallel capacitors and the reactors have low investment cost and are easy to install, the method is generally suitable for medium and small photovoltaic power stations with low access voltage levels.
S2, a mathematical model of the reactive power optimization of the distribution network containing the photovoltaic power station is established, the optimal system operation cost is taken as an objective function, and the mathematical model comprises two parts of the cost of system active network loss and the cost of adding a reactive power compensation device.
At present, a photovoltaic inverter has certain reactive power regulation capacity, so that the reactive power regulation capacity of the photovoltaic inverter is taken into consideration when the reactive power optimization of a photovoltaic system is carried out. The grid-connected schematic diagram of the photovoltaic grid-connected inverter is shown in fig. 2. Wherein, UiFor the inverter output voltage, UsIs the grid voltage, delta is UiAnd UsL is the coupled inductance value and f is the grid system frequency. Active power output by systemThe power is as follows:
the reactive power output by the system is as follows:
the active output of the system is the active power generated by the photovoltaic system, and the reactive output of the system depends on the weather conditions such as sunlight, temperature and the like, and the regulation of grid-connected voltage UiAnd the grid-connected phase angle delta. In the control of the system, by changing UiAnd delta to realize independent adjustment of active and reactive power. When the photovoltaic array outputs energy, the inverter converts direct current into alternating current and transmits the alternating current to a power grid; meanwhile, certain reactive current is compensated for the power grid according to relevant requirements, and when the power value output by the photovoltaic array is lower than a limit value and stops outputting, the photovoltaic inverter can still perform certain reactive compensation on the power grid. Therefore, the power quality of the power grid can be improved, and the utilization rate of the system can be improved. After the reactive output capacity of the photovoltaic power station is considered, not only can the investment cost of the reactive compensation device be saved, but also the electric energy quality of the power distribution network is improved, so that the reactive output capacity of the photovoltaic power station is considered to be necessary when the reactive optimization of the photovoltaic system is carried out.
The power distribution network reactive power optimization of the photovoltaic power station is carried out, and a mathematical model comprises 3 parts of an objective function, power equation equality constraint and inequality constraint. Terminal voltage U of selected generatoriOutput Q of reactive power compensation deviceCAnd the transformation ratio T of the on-load tap changertAs a control variable, the load node voltage value UDAs state variables.
(1) Objective function
Selecting the optimal system operation as a target function, considering the network loss cost after reactive compensation and the expense cost for adding a reactive compensation device, and adopting the following expression:
min F＝βτmaxΔPΣ+(α+γ)KCQCΣ(6)
wherein β is the price of electricity per degree, taumaxFor annual maximum load loss hours, α and gamma respectively represent annual depreciation maintenance rate and investment recovery rate of reactive compensation equipment, KCPrice per unit capacity of reactive power compensator, QCΣIs the sum of the reactive compensation capacities, Δ PΣThe network loss after reactive compensation.
(2) Power equation equality constraints
The active power and reactive power of each node are constrained as follows:
wherein N is the number of load nodes, GijIs the conductance between i, j, BijIs the susceptance between i, j, deltaijIs the phase angle difference between i, j.
(3) Constraint of inequality
The variable constraints in the reactive power optimization problem of the power distribution network comprise control variable constraints and state variable constraints. The control variable constraints are:
Ui.min≤Ui≤Ui.max(9)
QC.min≤QC≤QCmax(10)
Tt.min≤Tt≤Tt.max(11)
the state variable constraints are:
UD.min≤UD≤UD.max(12)
wherein, UiIs the system generator terminal voltage (i is 1,2.. G, G is the number of generators), Ui.max,Ui.minThe upper and lower limits of the generator terminal voltage of the system are set; qCFor the capacity of the reactive power compensation means, QC.max,QC.minThe upper limit and the lower limit of the capacity of the reactive power compensation device are set; t istFor ratio change of on-load tap-changing transformers, Tt.max,Tt.minThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; u shapeDIs the value of the node voltage, UD.max,UD.minThe upper and lower limits of the node voltage value.
In the reactive power optimization problem, the state variable constraint can be treated by a penalty function method. The penalty function method is to add an out-of-range inequality constraint to an original objective function in the form of a penalty term to form a new objective function. Applying a penalty function method, wherein the reactive power optimization objective function is as follows:
wherein:
wherein, UDiIs the voltage value of the ith node, UDi.max,UDi.minThe voltage values of the ith node are upper and lower limits, lambda is a penalty coefficient, and N is the number of system nodes.
S3 Quantum Particle Swarm Optimization (QPSO) algorithm is introduced to solve the established reactive power Optimization model, the model is adopted to apply the QPSO algorithm to carry out example solution on the IEEE14 node system, and the power flow calculation is carried out by using a Matpower tool packet in MATLAB to obtain the network loss.
Standard particle swarm algorithm(Particle Swarm Optimization, PSO) Kennedy and Eberhart, 1995, simulated the process of birds looking for food for Optimization purposes. In PSO, each particle is a solution in the solution space. Let each particle be D-dimensional, m particles, where the spatial position of the ith particle is xi＝(xi1,xi2,...xiD) Velocity vector is vi＝(vi1,vi2,...viD) The self-searched optimal position is Pi＝(pi1,pi2,...piD) The optimum position p searched by the whole particle swarmg＝(pg1,pg2,...pgD). The updated iterative formulas of the velocity and the position of the particle are respectively as follows:
vi，，j(t+1)＝wvi,j(t)+c1r1(Pi，,j(t)-xi,j(t))+c2r2(Pg，,j(t)-xi,j(t)) (15)
xi，j(t+1)＝xi,j(t)+vi,j(t+1),j＝1,2...D (16)
in the formula: i 1,2,. m; w is an inertia factor; c. C1,c2Self factors and global factors; r is1,r2Is [0,1]]A random number in between. Pi,PgRespectively the optimal position experienced by the particle i and the optimal position experienced by all particles in the population.
In the development process of the basic particle swarm algorithm, researchers put forward the concept of the shrinkage factor and research the particle swarm algorithm with the shrinkage factor chi, and the algorithm describes a selection w, c1And c2Value method to ensure algorithm convergence. The speed update iterative formula of the algorithm is as follows:
wherein,l＝c1+c2。
the particle convergence track in the traditional particle swarm algorithm is in an orbit form, the particle flight speed is limited, and the upper limit value limits the search space of the particles, so that the algorithm cannot effectively jump out of a local optimal solution and cannot search a global optimal solution with probability 1. The convergence speed of the algorithm is reduced when the upper limit value of the speed is set to be too large, and the global search capability is reduced when the upper limit value of the speed is set to be too small, so that the search performance of the PSO algorithm depends on the convergence speed of the particle swarm speed to a great extent, and the robustness of the algorithm is reduced. The invention provides a QPSO algorithm applied to reactive power optimization of a photovoltaic system, wherein a Quantum Particle Swarm Optimization (QPSO) algorithm applies quantum mechanics to the QPSO algorithm, each individual in the QPSO algorithm can be described by one particle in a quantum space, and a quantum delta potential well model is established according to the intelligent aggregation of a group. The particles in the quantum space satisfy the property of satisfying the aggregation state, and can be searched in the whole feasible solution space. In quantum space, the position and speed of the particle cannot be determined simultaneously, and the position update iterative formula of the particle is obtained by referring to a wave function (grand jun, fangwei, wu xiajun, wenbo, quantum behavior particle swarm optimization: principle and application [ M ]. beijing: qinghua university press, 2011.):
wherein t is a current evolutionary algebra, p is a local attraction potential of the particles, represents a random position of each particle between a local optimal position and a global optimal position, and is determined by the following formula:
pij(t)＝φPij(t)+(1-φ)Pgj(t),j＝1,2...D (19)
wherein, Pij(t) is the local optimum position of the t-th generation of particle i, Pgj(t) is the global optimum position of the t-th generation, and phi represents the interval [0,1]]Uniformly distributed random numbers.L represents the weighted distance of the particle from the population mean optimum position:
L(t+1)＝2β·|m(t)-X(t)| (20)
wherein m (t) is the average optimal position, defined as the average of the local optimal values of all particles in the population:
β is a contraction and expansion coefficient, is the only parameter of QPSO, has the function of controlling the convergence speed of the particles, and adopts a change method that the parameter of QPSO is linearly reduced along with the evolution algebraic change to take values:
β＝(a-b)*(Gmax-t)/Gmax+b (22)
wherein a is 1, b is 0.5, GmaxIs the maximum evolutionary algebra.
According to the above equations, the iterative equation of the position of the particle in the QPSO algorithm is:
where u is a random number subject to uniform distribution over the interval [0,1 ].
The method for applying the quantum particle swarm algorithm to the reactive power optimization of the photovoltaic system specifically comprises the following steps:
1) reading in original data: the parameters (control variable description, constraint conditions and the like) of the power system, the parameter value range and initial value of the QPSO algorithm and the like are used for randomly generating an initialization population Q (t)0) Randomly generating an initial position within a control variable constraintInitializing a global optimal variable;
2) by means of a target letterNumber F (x)0) And (3) evaluating a fitness function, sending the initial control variable into a Matpower toolkit of the MATLAB for load flow calculation to obtain the network loss, wherein the target function is as follows:
evaluating by taking an objective function F (x) as a fitness function;
3) record Q (t)0) Taking the medium optimal fitness function value as a local optimal fitness value and a corresponding optimal individual;
4) comparing the local optimal adaptation value with the global optimal adaptation value, and updating and recording the global optimal value and the corresponding optimal individual;
5) and (3) taking random numbers u obeying uniform distribution in the interval [0,1], and updating the positions of the particle swarms according to a particle position updating formula:
if u is less than 0.5,
if u is greater than 0.5, then,
calculating the network loss by Matpower according to the control variable value at the moment to obtain cost F (x);
6) comparing the adaptive value with the local optimal position and the global optimal position of the particle swarm, and updating and recording the local optimal position, the global optimal position and the corresponding optimal individual at the moment;
7) repeating the steps 5) -6) until the maximum evolution algebra G is reachedmaxAnd recording the optimal control variable value and calculating the network loss and the cost F.
The method of the invention is verified by taking the IEEE14 node system shown in FIG. 5 as a research object.
The invention modifies IEEE14 node system: a photovoltaic power station (PV) is connected to a node 11, and the output is 50 MW; a reactive power compensation device is connected to a node 9, the upper compensation limit is 50Mvar, 5 gears are shared, and the step length is 10; the system has 3 transformers, the transformation ratio adjusting range is [0.9,1.1], 9 gears are shared, and the step length is 2.5%. The value range of each node voltage is [0.95,1.1], and the initial system network loss is 13.39 (per unit value).
And optimizing the system by adopting a quantum particle swarm algorithm and comparing the optimized result with the optimized result of the particle swarm algorithm with the shrinkage factor. Parameter values c in the algorithm1＝2.05，c22.05, population number 100, number of iterations 50, β 0.2/kW · h, τmax5000h, α and gamma are 0.1, KC100 yuan/kvar. A comparison graph of the unoptimized node voltage values and the node voltage values optimized by PSO and QPSO is shown in fig. 6, wherein a part of the node voltage values are selected for tabulation, and the results before and after reactive power optimization are shown in table 1.
TABLE 1IEEE14 node system reactive power optimization pre-post comparison
The comparison of the number of iterations after PSO and QPSO reactive optimization with the objective function values is shown in fig. 7. According to the grid loss and the system operation cost, the grid loss can be reduced by carrying out reactive power optimization on the grid system containing the photovoltaic power station, and therefore the operation cost is reduced. The PSO algorithm and the QPSO algorithm can obtain a relatively ideal optimization effect, wherein the QPSO algorithm has stronger global search capability and can effectively get rid of a local optimal solution. Through comparison of the voltage values of the nodes before and after the reactive power optimization, the node voltage is increased to a certain degree after the optimization, and the system is more stable in operation. As can be seen from fig. 7, compared with the PSO algorithm, the QPSO algorithm has a stronger advantage, and can get rid of 999 ten thousand yuan of the local optimal solution in a shorter time and search for 988 ten thousand yuan of the better solution. The running cost of the system optimized by the QPSO algorithm is 11 ten thousand yuan less than that of the system optimized by the PSO algorithm.
The principles and embodiments of the present invention are explained herein using specific preferred embodiments, which are described only to help understand the method and its core idea of the present invention; meanwhile, for a person skilled in the art, the specific embodiments and the application range may be changed according to the idea of the present invention. In summary, this summary should not be construed to limit the present invention.
Claims (9)
1. A photovoltaic system reactive power optimization method based on a quantum particle swarm algorithm is characterized by comprising the following steps: comprises the following steps of (a) carrying out,
s1, analyzing the influence of grid connection of the photovoltaic power station on the voltage of the power distribution network system;
s2, establishing a reactive power optimization mathematical model of the distribution network containing the photovoltaic power station;
s3, solving the reactive power optimization mathematical model of the power distribution network of the photovoltaic power station by using a quantum particle swarm optimization algorithm.
2. The photovoltaic system reactive power optimization method based on the quantum-behaved particle swarm optimization algorithm according to claim 1, wherein the method comprises the following steps: the S1 includes the steps of,
s11, the voltage drop of the two ends of the line in the simple grid-connected circuit of the photovoltaic power station is expressed as the following voltage drop expression,
dU＝ΔU+jδU
wherein, suppose that the phase angle of the voltage of the line at the terminal is 0, delta is the phase angle difference of the voltage, P is the active power, Q is the reactive power, R is the resistance, X is the reactance, U1For the output voltage, P, of the photovoltaic power station1、Q1Respectively, the active power of the photovoltaic power station, the reactive power of the photovoltaic power station, U2For system grid-connected voltage, P2，Q2Respectively are grid-connected active power and grid-connected reactive power;
s12, simplifying the voltage drop expression in S11, wherein the grid voltage impedance R is greater than X, the grid voltage impedance is of a resistance characteristic, the reactance X is ignored, and the voltage drop expression is as follows:
3. the photovoltaic system reactive power optimization method based on the quantum-behaved particle swarm algorithm according to claim 2, characterized in that: the reactive power optimization mathematical model in S2 includes an objective function, a power equation constraint, and an inequality constraint.
4. The reactive power optimization method of the photovoltaic system based on the quantum-behaved particle swarm algorithm, according to claim 3, is characterized in that: the expression of the objective function is as follows,
min F＝βτmaxΔPΣ+(α+γ)KCQCΣ
wherein β is the price of electricity per degree, taumaxIs the yearThe maximum load loss hours, α and gamma respectively represent the annual depreciation maintenance rate and the investment recovery rate of the reactive compensation equipment, KCPrice per unit capacity of reactive power compensator, QCΣIs the sum of the reactive compensation capacities, Δ PΣThe network loss after reactive compensation.
5. The reactive power optimization method of the photovoltaic system based on the quantum-behaved particle swarm algorithm, according to claim 4, is characterized in that: the power equation is constrained by the equation expressed as,
wherein i, j is a load node, N is the number of load nodes, GijIs the conductance between i, j, BijIs the susceptance between i, j, deltaijIs the phase angle difference between i, j.
6. The reactive power optimization method of the photovoltaic system based on the quantum-behaved particle swarm algorithm, according to claim 5, is characterized in that: the inequality constraints include control variable constraints and state variable constraints, wherein the expression of the control variable constraints is,
Ui.min≤Ui≤Ui.max
QC.min≤QC≤QCmax
Tt.min≤Tt≤Tt.max
the expression of the state variable constraint is,
UD.min≤UD≤UD.max
wherein, UiIs the system generator terminal voltage (i is 1,2.. G, G is the number of generators), Ui.max,Ui.minThe upper and lower limits of the generator terminal voltage of the system are set; qCFor the capacity of the reactive power compensation means, QC.max,QC.minThe upper limit and the lower limit of the capacity of the reactive power compensation device are set; t istFor ratio change of on-load tap-changing transformers, Tt.max,Tt.minThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; u shapeDIs the value of the node voltage, UD.max,UD.minThe upper and lower limits of the node voltage value.
7. The reactive power optimization method of the photovoltaic system based on the quantum-behaved particle swarm algorithm according to claim 6, characterized in that: and the constraint of the objective function and the state variable is processed by adopting a penalty function method, and the reactive power optimization objective function is,
wherein,
wherein, UDiIs the voltage value of the ith node, UDi.max,UDi.minThe voltage values of the ith node are upper and lower limits, lambda is a penalty coefficient, and N is the number of system nodes.
8. The reactive power optimization method for photovoltaic system based on quantum particle swarm optimization according to claim 6 or 7, wherein the quantum particle swarm optimization in S3 comprises:
s31, setting parameters including population size nPop of quantum particle swarm operation and maximum evolution algebra GmaxD, c, and D, wherein the contraction-expansion factor β is 0.75, the initial global optimum adaptation value is infinity, and the position x of the ith particlei＝(xi1,xi2..xiD)；
S32, evaluation Q (t)0) The fitness function value of (a) is evaluated by taking the objective function F (x) as a fitness function;
s33, record Q (t)0) Taking the medium optimal fitness function value as a local optimal fitness value and recording a corresponding optimal individual;
s34, comparing the local optimal adaptive value with the global optimal adaptive value, and updating and recording the global optimal value and the corresponding optimal individual;
s35, carrying out evolution, and updating a contraction-expansion coefficient β by adopting a change method that parameters of a quantum particle swarm algorithm linearly reduce along with evolution algebra change:
β＝(a-b)*(Gmax-t)/Gmax+b
wherein, a is 1, b is 0.5, and t is the current generation evolution algebra;
s36, updating the position, namely updating the position of the particle in the feasible space, wherein the position updating formula of the particle is as follows:
wherein L represents a weighted distance between the particle and the population average optimal position, u is a random number obeying uniform distribution over the interval [0,1], p is a local attraction potential of the particle, represents a random position of each particle between the local optimal position and the global optimal position, and is determined by the following formula:
pij(t)＝φPij(t)+(1-φ)Pgj(t),j＝1,2...D
wherein, Pij(t) is the local optimum position of the t-th generation of particle i, Pgj(t) is the global optimum position of the t-th generation, and phi represents the interval [0,1]]The random number is uniformly distributed, and L represents the weighted distance between the particle and the average optimal position of the population:
L(t+1)＝2β·|m(t)-X(t)|
wherein m (t) is the average optimal position, defined as the average of the local optimal values of all particles in the population:
s37, taking the random number u uniformly distributed in the interval [0,1], and updating the position of the particle i:
if u is less than 0.5,
if u is greater than 0.5, then,
s38, calculating the adaptive value F (x) of the particles at the moment, and updating and recording the local optimal value, the global optimal value and the corresponding optimal individual;
s39, judging whether the operation of the quantum particle swarm optimization algorithm reaches the maximum evolution algebra GmaxIf the maximum evolution algebra G is not reachedmaxReturning to S35 and performing the subsequent steps in order; if the maximum evolution algebra G is reachedmaxAnd the algorithm ends.
9. The reactive power optimization method of the photovoltaic system based on the quantum-behaved particle swarm algorithm according to claim 8, characterized in that: the S3 specifically comprises
S41, reading in original data, wherein the original data comprises power system parameters, parameter value range and initial value of quantum particle swarm optimization, and randomly generating an initialization population Q (t)0) Randomly generating an initial position within a control variable constraintInitializing a global optimal variable;
s42, using the objective function F (x)0) And (3) evaluating a fitness function, sending the initial control variable into a Matpower toolkit of the MATLAB for load flow calculation to obtain the network loss, wherein the target function is as follows:
evaluating by taking an objective function F (x) as a fitness function;
s43, record Q (t)0) Optimum adaptation inTaking the degree function value as a local optimal adaptive value and recording a corresponding optimal individual;
s44, comparing the local optimal adaptive value with the global optimal adaptive value, and updating and recording the global optimal value and the corresponding optimal individual;
s45, updating the formula according to the particle position:
updating the positions of the particle swarms, and calculating the network loss by Matpower according to the control variable values at the moment to obtain cost F (x);
s46, comparing the adaptive value with the local optimal position and the global optimal position of the particle swarm, and updating and recording the local optimal position, the global optimal position and the corresponding optimal individual at the moment;
s47, repeating the steps S45 and S46 until reaching the maximum evolution algebra GmaxThen, the optimum control variable value, the network loss and the cost F at the moment are recorded.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811241127.7A CN109274136A (en) | 2018-10-24 | 2018-10-24 | A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811241127.7A CN109274136A (en) | 2018-10-24 | 2018-10-24 | A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109274136A true CN109274136A (en) | 2019-01-25 |
Family
ID=65194149
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811241127.7A Pending CN109274136A (en) | 2018-10-24 | 2018-10-24 | A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109274136A (en) |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110311386A (en) * | 2019-07-26 | 2019-10-08 | 国网江苏省电力有限公司淮安供电分公司 | A kind of capacity configuration optimizing method based on the spontaneous idle and SVG reactive compensation of PSO new energy power station |
| CN110556851A (en) * | 2019-09-12 | 2019-12-10 | 云南电网有限责任公司临沧供电局 | power distribution network optimized voltage management method based on electric automobile power changing station |
| CN111259540A (en) * | 2020-01-14 | 2020-06-09 | 广东工业大学 | Energy efficiency analysis and optimization method for photovoltaic glass calendering and molding process |
| CN113127943A (en) * | 2021-03-01 | 2021-07-16 | 西安电子科技大学 | Distributed array optimization method based on genetic and quantum particle swarm optimization |
| CN113270869A (en) * | 2021-05-31 | 2021-08-17 | 河北工业大学 | Reactive power optimization method for photovoltaic power distribution network |
| CN113688576A (en) * | 2021-09-16 | 2021-11-23 | 国网浙江省电力有限公司宁波供电公司 | Urban area energy Internet energy scheduling method |
| CN115001056A (en) * | 2022-06-22 | 2022-09-02 | 北京城建设计发展集团股份有限公司 | Distributed reactive compensation method for urban rail transit based on bidirectional converter |
| CN115133541A (en) * | 2022-07-27 | 2022-09-30 | 国网山东省电力公司枣庄供电公司 | Improved particle swarm algorithm-based reactive power compensation method and system for photovoltaic power generation system |
| CN116362141A (en) * | 2023-06-01 | 2023-06-30 | 中诚华隆计算机技术有限公司 | Reactive power optimization method and device for power distribution network based on quantum genetic algorithm |
| CN117639082A (en) * | 2024-01-26 | 2024-03-01 | 南京师范大学 | Multi-target reactive power optimization method for power distribution network based on improved quantum particle swarm algorithm |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105470947A (en) * | 2015-11-23 | 2016-04-06 | 杭州电子科技大学 | Micro-power-grid scheduling method based on quantum-behaved particle swarm optimization |
| CN105958482A (en) * | 2016-05-31 | 2016-09-21 | 天津天大求实电力新技术股份有限公司 | Micro-grid optimization method based on good point set quantum particle swarm algorithm |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
-
2018
- 2018-10-24 CN CN201811241127.7A patent/CN109274136A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105470947A (en) * | 2015-11-23 | 2016-04-06 | 杭州电子科技大学 | Micro-power-grid scheduling method based on quantum-behaved particle swarm optimization |
| WO2017161785A1 (en) * | 2016-03-23 | 2017-09-28 | 严利容 | Method for controlling stable photovoltaic power output based on energy storage running state |
| CN105958482A (en) * | 2016-05-31 | 2016-09-21 | 天津天大求实电力新技术股份有限公司 | Micro-grid optimization method based on good point set quantum particle swarm algorithm |
Non-Patent Citations (3)
| Title |
|---|
| JUN SUN: "Quantum-Behaved Particle Swarm Optimization: Analysis of Individual Particle Behavior and Parameter Selection", 《EVOLUTIONARY COMPUTATION》 * |
| 孙卓新: "基于粒子群算法的含光伏电站的配电网无功优化", 《电力建设》 * |
| 杨宝杰: "基于量子粒子群算法的配电网分布式电源选址和定容", 《研究与分析》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110311386A (en) * | 2019-07-26 | 2019-10-08 | 国网江苏省电力有限公司淮安供电分公司 | A kind of capacity configuration optimizing method based on the spontaneous idle and SVG reactive compensation of PSO new energy power station |
| CN110556851A (en) * | 2019-09-12 | 2019-12-10 | 云南电网有限责任公司临沧供电局 | power distribution network optimized voltage management method based on electric automobile power changing station |
| CN111259540B (en) * | 2020-01-14 | 2023-04-11 | 广东工业大学 | Energy efficiency analysis and optimization method for photovoltaic glass calendering and molding process |
| CN111259540A (en) * | 2020-01-14 | 2020-06-09 | 广东工业大学 | Energy efficiency analysis and optimization method for photovoltaic glass calendering and molding process |
| CN113127943B (en) * | 2021-03-01 | 2023-05-05 | 西安电子科技大学 | Distributed array optimization method based on genetic and quantum particle swarm algorithm |
| CN113127943A (en) * | 2021-03-01 | 2021-07-16 | 西安电子科技大学 | Distributed array optimization method based on genetic and quantum particle swarm optimization |
| CN113270869A (en) * | 2021-05-31 | 2021-08-17 | 河北工业大学 | Reactive power optimization method for photovoltaic power distribution network |
| CN113688576A (en) * | 2021-09-16 | 2021-11-23 | 国网浙江省电力有限公司宁波供电公司 | Urban area energy Internet energy scheduling method |
| CN115001056A (en) * | 2022-06-22 | 2022-09-02 | 北京城建设计发展集团股份有限公司 | Distributed reactive compensation method for urban rail transit based on bidirectional converter |
| CN115133541A (en) * | 2022-07-27 | 2022-09-30 | 国网山东省电力公司枣庄供电公司 | Improved particle swarm algorithm-based reactive power compensation method and system for photovoltaic power generation system |
| CN116362141A (en) * | 2023-06-01 | 2023-06-30 | 中诚华隆计算机技术有限公司 | Reactive power optimization method and device for power distribution network based on quantum genetic algorithm |
| CN117639082A (en) * | 2024-01-26 | 2024-03-01 | 南京师范大学 | Multi-target reactive power optimization method for power distribution network based on improved quantum particle swarm algorithm |
| CN117639082B (en) * | 2024-01-26 | 2024-04-19 | 南京师范大学 | Multi-target reactive power optimization method for power distribution network based on improved quantum particle swarm algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109274136A (en) | A kind of photovoltaic system idle work optimization method based on quanta particle swarm optimization | |
| CN107634518B (en) | Source-network-load coordinated active power distribution network economic dispatching method | |
| CN109873447B (en) | Multi-time-level active-reactive power regulation and control method for multi-source cooperative active power distribution network | |
| CN110247438B (en) | Active power distribution network resource optimization configuration based on longicorn whisker algorithm | |
| CN107134789B (en) | Optical storage optimal power flow control method based on expansion QV node | |
| CN109687510A (en) | A kind of meter and probabilistic power distribution network Multiple Time Scales optimizing operation method | |
| Li et al. | Day-ahead optimal dispatch strategy for active distribution network based on improved deep reinforcement learning | |
| CN116388302B (en) | Active-reactive power combined optimization method for power distribution network for coordinating network side resources | |
| CN112671047B (en) | Active power distribution network reconstruction and reactive power joint robust optimization method considering limit scene | |
| CN111614110B (en) | Receiving-end power grid energy storage optimization configuration method based on improved multi-target particle swarm optimization | |
| CN114881346A (en) | Double-layer optimization scheduling method for dynamic reconstruction and reactive voltage adjustment based on improved whale optimization algorithm | |
| CN112564126A (en) | Power system network loss minimum reactive power optimization method based on improved differential evolution algorithm | |
| CN116667325A (en) | Micro-grid-connected operation optimization scheduling method based on improved cuckoo algorithm | |
| CN111144633A (en) | CCHP micro-grid operation optimization method | |
| CN108551177B (en) | Sensitivity analysis-based transient load shedding control optimization method for direct current receiving end system | |
| Li et al. | Deep reinforcement learning based continuous volt-var optimization in power distribution systems with renewable energy resources | |
| CN117913789A (en) | Improved multi-objective optimization control method for distributed optical storage participated power distribution network | |
| CN117891307A (en) | GA-PSO algorithm-based distributed photovoltaic maximum power tracking system and method | |
| Yu et al. | Research on the construction of new energy microgrids for wind power generation based on green and low carbon | |
| CN117172109A (en) | Capacity optimization method of off-grid photovoltaic hydrogen production system | |
| Pan et al. | Research on photovoltaic MPPT technique based on deep reinforcement learning under varying irradiance levels | |
| CN110829440B (en) | Three-phase medium-low voltage integrated power distribution network voltage control method and system | |
| Li et al. | The Fuzzy Power Flow Based Network Planning of Distribution Networks with Distributed Generation | |
| Sharbaf et al. | Placement and quantitating of FACTS devices in a power system including the wind unit to enhance system parameters | |
| Liu et al. | Collaborative optimization of economic dispatch and reactive power dispatch |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190125 |