Title :
An evolutionary algorithm and acceleration approach for topological design of distributed resource islands
Author :
Giraldez, J. ; Jaiantilal, A. ; Walz, J. ; Suryanarayanan, S. ; Sankaranarayanan, Sriram ; Brown, H.E. ; Chang, En-Jui
Abstract :
In response to the ongoing discussion on how electric power distribution systems should evolve under the Smart Grid Initiative, an optimization problem is defined to simultaneously determine optimal locations for Distributed Generation (DG) and feeder interties in a legacy radial distribution system to improve reliability in the islanded mode of operation. For that purpose, an evolutionary approach using the Multi Objective Genetic Algorithm (MOGA) is formulated. The choice of an evolutionary algorithmic approach is justified due to the intractability of the problem associated with optimal location of DGs and feeder interties for large distribution systems. Furthermore, we present a filtering technique using machine learning (ML) for improving performance by avoiding expensive simulations for potentially suboptimal inputs. The algorithm is applied to a test system in which two methods of expressing the load are explored. In both cases, similar satisfactory design solutions are obtained.
Keywords :
distributed power generation; evolutionary computation; filtering theory; genetic algorithms; learning (artificial intelligence); power distribution planning; power distribution reliability; power engineering computing; smart power grids; DG optimal location; ML; MOGA; acceleration approach; distributed generation; distributed resource island topological design; distribution planning tool; electric power distribution systems; evolutionary algorithm; filtering technique; legacy radial distribution system; machine learning; multiobjective genetic algorithm; optimization problem; reliability; smart grid initiative; Biological cells; Genetic algorithms; Machine learning; Optimization; Power system reliability; Reliability; Training; Distributed generation; Smart Grid; distributed island resource; distribution system planning; genetic algorithm; machine learning; microgrids; multi-objective optimization; reliability; renewable energy sources;
Conference_Titel :
PowerTech, 2011 IEEE Trondheim
Conference_Location :
Trondheim
Print_ISBN :
978-1-4244-8419-5
Electronic_ISBN :
978-1-4244-8417-1
DOI :
10.1109/PTC.2011.6019258