Reactive power optimization in large scale power systems

1992 ◽  
Vol 14 (4) ◽  
pp. 276-283 ◽  
Author(s):  
M. Yehia ◽  
I. Ghandour ◽  
M. Saidy ◽  
V.A. Stroev
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Reactive Power ◽  
Power Optimization ◽  
Reactive Power Optimization

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.

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.

scholarly journals Opposition-Based Improved PSO for Optimal Reactive Power Dispatch and Voltage Control

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Shengrang Cao ◽  
Xiaoqun Ding ◽  
Qingyan Wang ◽  
Bingyan Chen
Keyword(s):  
Power Systems ◽  
Life Cycle Cost ◽  
Reactive Power ◽  
Power Optimization ◽  
Optimization Problems ◽  
Cost Model ◽  
Population Diversity ◽  
Reactive Power Optimization ◽  
Switching Operation ◽  
Global And Local

An opposition-based improved particle swarm optimization algorithm (OIPSO) is presented for solving multiobjective reactive power optimization problem. OIPSO uses the opposition learning to improve search efficiency, adopts inertia weight factors to balance global and local exploration, and takes crossover and mutation and neighborhood model strategy to enhance population diversity. Then, a new multiobjective model is built, which includes system network loss, voltage dissatisfaction, and switching operation. Based on the market cost prices, objective functions are converted to least-cost model. In modeling process, switching operation cost is described according to the life cycle cost of transformer, and voltage dissatisfaction penalty is developed considering different voltage quality requirements of customers. The experiment is done on the new mathematical model. Through the simulation of IEEE 30-, 118-bus power systems, the results prove that OIPSO is more efficient to solve reactive power optimization problems and the model is more accurate to reflect the real power system operation.

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