Power loss minimization in electrical power distribution networks by use of hybrid reconfiguration method

2017 ◽  
Author(s):  
Birender Singh ◽  
Sushil Chauhan ◽  
C.C. Reddy
Keyword(s):  
Power Distribution ◽  
Power Loss ◽  
Electrical Power ◽  
Distribution Networks ◽  
Loss Minimization ◽  
Power Distribution Networks ◽  
Power Loss Minimization

Distributed Generation Placement for Power Distribution Networks

2014 ◽  
Vol 24 (01) ◽  
pp. 1550009 ◽  
Author(s):  
Xiaodao Chen ◽  
Shiyan Hu
Keyword(s):  
Smart Grid ◽  
Distributed Generation ◽  
Power Distribution ◽  
Power Loss ◽  
Distribution Systems ◽  
Optimization Technique ◽  
Electrical Power ◽  
Distribution Networks ◽  
Ocean Energy ◽  
Electrical Power System

Growing concerns on the energy crisis impose great challenges in development and deployment of the smart grid technologies into the existing electrical power system. A key enabling technology in smart grid is distributed generation, which refers to the technology that power generating sources are located in a highly distributed fashion and each customer is both a consumer and a producer for energy. An important optimization problem in distributed generation design is the insertion of distributed generators (DGs), which are often renewable resources exploiting e.g., photovoltaic, hydro, wind, ocean energy. In this paper, a new power loss filtering based sensitivity guided cross entropy (CE) algorithm is proposed for the distributed generator insertion problem. This algorithm is based on the advanced CE optimization technique which exploits the idea of importance sampling in performing optimization. Our experimental results demonstrate that on large distribution networks, our algorithm can largely reduce (up to 179.3%) power loss comparing to a state-of-the-art sensitivity guided greedy algorithm with small runtime overhead. In addition, our algorithm runs about 5× faster than the classical CE algorithm due to the integration of power loss filtering and sensitivity optimization. Moreover, all existing techniques only test on very small distribution systems (usually with < 50 nodes) while our experiments are performed on the distribution networks with up to 5000 nodes, which matches the realistic setup. These demonstrate the practicality of the proposed algorithm.

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