Reactive power service cost allocation using Aumann-Shapley method

2006 ◽  
Vol 153 (5) ◽  
pp. 540 ◽  
Author(s):  
X.J. Lin ◽  
C.W. Yu ◽  
N. Xu ◽  
C.Y. Chung ◽  
H. Wu
Keyword(s):  
Cost Allocation ◽  
Reactive Power ◽  
Service Cost ◽  
Reactive Power Service

A cost allocation method for reactive power service based on power flow tracing

2003 ◽  
Vol 64 (1) ◽  
pp. 59-65 ◽  
Author(s):  
Y. Dai ◽  
X.D. Liu ◽  
Y.X. Ni ◽  
F.S. Wen ◽  
Z.X. Han ◽  
...  
Keyword(s):  
Power Flow ◽  
Cost Allocation ◽  
Reactive Power ◽  
Allocation Method ◽  
Power Flow Tracing ◽  
Reactive Power Service

Linear Sensitivities to Define Reactive Power Areas for Voltage Control and Reactive Power Service in Electricity Markets

2015 ◽  
Vol 13 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Jose H. Tovar Hernandez ◽  
Guillermo Gutierrez Alcaraz ◽  
Ricardo A. Solera Urcuyo
Keyword(s):  
Electricity Markets ◽  
Reactive Power ◽  
Voltage Control ◽  
Reactive Power Service

Reactive Power Compensation of the Distribution Power System with Distributed Generation Using Improved Tabu Search Algorithm

2013 ◽  
Vol 765-767 ◽  
pp. 2503-2508
Author(s):  
Xiang Lei ◽  
Yan Li ◽  
Shao Rong Wang ◽  
Hong Zhao ◽  
Fen Zhou ◽  
...  
Keyword(s):  
Tabu Search ◽  
Distributed Generation ◽  
Power Loss ◽  
Cost Allocation ◽  
Reactive Power ◽  
Search Algorithm ◽  
Test System ◽  
Local Optimum ◽  
Tabu Search Algorithm ◽  
Optimal Position

Taking account of the mutual impacts of distributed generation and reactive power, to determine the optimal position and capacity of the compensation device to be installed, the paper proposed an improved Tabu search algorithm for reactive power optimization. The voltage quality is considered of the model using minimum network active power loss as objective Function. It is achieved by maintaining the whole system power loss as minimum thereby reducing cost allocation. On the basis of general Tabu search algorithm, the algorithm used memory guidance search strategy to focus on searching for a local optimum value, avoid a global search blindness. To deal with the neighborhood solution set properly and save algorithm storage space , some corresponding improvements are made, thus, it is easily to stop the iteration of partial optimization and it is more probable to achieve the global optimization by use of the improved algorithm. Simulations are carried out on standard IEEE 33 test system and results are presented.

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