scholarly journals Research on reactive power optimization of district power network with large-scale new energy

2015 ◽  
Author(s):  
Dan Wang ◽  
Jingjing Tong ◽  
Jianhua Zhang
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Power Network ◽  
Reactive Power Optimization ◽  
New Energy

scholarly journals Multiobjective Reactive Power Optimization of Renewable Energy Power Plants Based on Time-and-Space Grouping Method

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3556
Author(s):  
Linan Qu ◽  
Shujie Zhang ◽  
Hsiung-Cheng Lin ◽  
Ning Chen ◽  
Lingling Li
Keyword(s):  
Renewable Energy ◽  
Power Plants ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Compensation ◽  
Mixed Integer ◽  
Reactive Power Optimization ◽  
Time And Space ◽  
Bus Voltage

The large-scale renewable energy power plants connected to a weak grid may cause bus voltage fluctuations in the renewable energy power plant and even power grid. Therefore, reactive power compensation is demanded to stabilize the bus voltage and reduce network loss. For this purpose, time-series characteristics of renewable energy power plants are firstly reflected using K-means++ clustering method. The time group behaviors of renewable energy power plants, spatial behaviors of renewable energy generation units, and a time-and-space grouping model of renewable energy power plants are thus established. Then, a mixed-integer optimization method for reactive power compensation in renewable energy power plants is developed based on the second-order cone programming (SOCP). Accordingly, power flow constraints can be simplified to achieve reactive power optimization more efficiently and quickly. Finally, the feasibility and economy for the proposed method are verified by actual renewable energy power plants.

scholarly journals A Multistage Solution Approach for Dynamic Reactive Power Optimization Based on Interval Uncertainty

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Xiaodong Shen ◽  
Yang Liu ◽  
Yan Liu
Keyword(s):  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Programming Model ◽  
Nonconvex Programming ◽  
Interval Uncertainty ◽  
Mixed Integer ◽  
Reactive Power Optimization ◽  
Renewable Energy Resources ◽  
Integer Quadratic Programming

In order to solve the uncertainty and randomness of the output of the renewable energy resources and the load fluctuations in the reactive power optimization, this paper presents a novel approach focusing on dealing with the issues aforementioned in dynamic reactive power optimization (DRPO). The DRPO with large amounts of renewable resources can be presented by two determinate large-scale mixed integer nonlinear nonconvex programming problems using the theory of direct interval matching and the selection of the extreme value intervals. However, it has been admitted that the large-scale mixed integer nonlinear nonconvex programming is quite difficult to solve. Therefore, in order to simplify the solution, the heuristic search and variable correction approaches are employed to relax the nonconvex power flow equations to obtain a mixed integer quadratic programming model which can be solved using software packages such as CPLEX and GUROBI. The ultimate solution and the performance of the presented approach are compared to the traditional methods based on the evaluations using IEEE 14-, 118-, and 300-bus systems. The experimental results show the effectiveness of the presented approach, which potentially can be a significant tool in DRPO research.

Reactive Power Optimization Problem Research Based on Node-Voltage-Based and Branch-Current-Based Hybrid Electric Power Network Equations

2012 ◽  
Vol 614-615 ◽  
pp. 751-760
Author(s):  
Guo You Wang ◽  
Xi Lin Zhang ◽  
Yu Shi ◽  
Yang Liu ◽  
Cheng Min Wang ◽  
...  
Keyword(s):  
Electric Power ◽  
Computational Efficiency ◽  
Optimization Problem ◽  
Reactive Power ◽  
Power Optimization ◽  
Electric Power System ◽  
Power Network ◽  
Reactive Power Optimization ◽  
Hybrid Electric ◽  
Node Voltage

The electric power system is a specific example among various networks in nature and human society, in which the network flow models and arithmetic can be applied. The node-voltage-based and branch-current-based hybrid electric power network equations are established in this paper, and the reactive power optimization problem is modeled based on the established network equations. It is respectively solved while the reactive power optimization problem is decomposed as two sub-problems, among which a sub-problem is described by quadric minimum cost flow model and another one is expressed by a linear equations. Thereby, the complexity and dimensions of reactive power optimization problem are distinctly reduced due to the two decomposed sub-problems are easy to solve. It is proved that found optimal solution is closed to global by the computational efficiency analysis. The case study is made at IEEE-30 system and it is indicated that proposed approach could improve the computational efficiency of reactive power optimization problem by comparing with traditional optimal power flow arithmetic.

scholarly journals Reactive Power Optimization of Large-Scale Power Systems: A Transfer Bees Optimizer Application

Processes ◽  
2019 ◽  
Vol 7 (6) ◽  
pp. 321 ◽  
Author(s):  
Huazhen Cao ◽  
Tao Yu ◽  
Xiaoshun Zhang ◽  
Bo Yang ◽  
Yaxiong Wu
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Solution Space ◽  
Reactive Power Optimization ◽  
Dimensional Solution ◽  
Q Learning ◽  
State Action ◽  
Low Dimensional

A novel transfer bees optimizer for reactive power optimization in a high-power system was developed in this paper. Q-learning was adopted to construct the learning mode of bees, improving the intelligence of bees through task division and cooperation. Behavior transfer was introduced, and prior knowledge of the source task was used to process the new task according to its similarity to the source task, so as to accelerate the convergence of the transfer bees optimizer. Moreover, the solution space was decomposed into multiple low-dimensional solution spaces via associated state-action chains. The transfer bees optimizer performance of reactive power optimization was assessed, while simulation results showed that the convergence of the proposed algorithm was more stable and faster, and the algorithm was about 4 to 68 times faster than the traditional artificial intelligence algorithms.

Sign in / Sign up

Export Citation Format

Share Document