DOI:https://doi.org/10.1007/s40565-018-0480-7 |
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Distribution network reconfiguration using feasibility-preserving evolutionary optimization |
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Net amount: 1058 |
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Author:
Alberto LANDEROS1
, Slawomir KOZIEL1,2, Mohamed F. ABDEL-FATTAH1
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Author Affiliation:
1. School of Science and Engineering, Reykjavik University,
101 Reykjavik, Iceland
2. Faculty of Electronics, Telecommunications and Informatics,
Gdansk University of Technology, 80-233 Gdan´sk, Poland
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Foundation: |
This work was supported in part by Mexico’s
National Council for Science and Technology-Sustentabilidad Energetica SENER CONACYT (2016) and National Science Centre of
Poland Grant 2014/15/B/ST8/02315. |
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Abstract: |
Distribution network reconfiguration (DNR) can
significantly reduce power losses, improve the voltage
profile, and increase the power quality. DNR studies
require implementation of power flow analysis and complex optimization procedures capable of handling large
combinatorial problems. The size of distribution network
influences the type of the optimization method to be
applied. Straightforward approaches can be computationally expensive or even prohibitive whereas heuristic or
meta-heuristic approaches can yield acceptable results with
less computation cost. In this paper, a customized evolutionary algorithm has been introduced and applied to power
distribution network reconfiguration. The recombination
operators of the algorithm are designed to preserve feasibility of solutions (radial structure of the network) thus
considerably reducing the size of the search space. Consequently, improved repeatability of results as well as
lower overall computational complexity of the optimization process have been achieved. The optimization process
considers power losses and the system voltage profile, both
aggregated into a scalar cost function. Power flow analysis
is performed with the Open Distribution System Simulator,
a simple and efficient simulation tool for electric distribution systems. Our approach is demonstrated using several networks of various sizes. Comprehensive
benchmarking indicates superiority of the proposed technique over state-of-the-art methods from the literature. |
Keywords: |
Distribution network reconfiguration,
Feasibility-preserving evolutionary optimization, Power
loss reduction, Radial networks, Voltage profile |
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Online Time:2019/05/14 |
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