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Energies 2015, 8(12), 13641-13659; doi:10.3390/en81212389

Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem

School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, China
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Author to whom correspondence should be addressed.
Academic Editor: Francesco Calise
Received: 14 October 2015 / Revised: 22 November 2015 / Accepted: 25 November 2015 / Published: 1 December 2015
(This article belongs to the Special Issue Simulation of Polygeneration Systems)
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Abstract

Distributed generation (DG) systems are integral parts in future distribution networks. In this paper, a novel approach integrating crisscross optimization algorithm and Monte Carlo simulation (CSO-MCS) is implemented to solve the optimal DG allocation (ODGA) problem. The feature of applying CSO to address the ODGA problem lies in three interacting operators, namely horizontal crossover, vertical crossover and competitive operator. The horizontal crossover can search new solutions in a hypercube space with a larger probability while in the periphery of each hypercube with a decreasing probability. The vertical crossover can effectively facilitate those stagnant dimensions of a population to escape from premature convergence. The competitive operator allows the crisscross search to always maintain in a historical best position to quicken the converge rate. It is the combination of the double search strategies and competitive mechanism that enables CSO significant advantage in convergence speed and accuracy. Moreover, to deal with system uncertainties such as the output power of wind turbine and photovoltaic generators, an MCS-based method is adopted to solve the probabilistic power flow. The effectiveness of the CSO-MCS method is validated on the typical 33-bus and 69-bus test system, and results substantiate the suitability of CSO-MCS for multi-objective ODGA problem. View Full-Text
Keywords: distributed generation; optimal allocation; crisscross optimization algorithm; Monte Carlo simulation; uncertainties distributed generation; optimal allocation; crisscross optimization algorithm; Monte Carlo simulation; uncertainties
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Peng, X.; Lin, L.; Zheng, W.; Liu, Y. Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem. Energies 2015, 8, 13641-13659.

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