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

Xiangang Peng, Lixiang Lin, Weiqin Zheng and Yi Liu
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Xiangang Peng: School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, China
Lixiang Lin: School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, China
Weiqin Zheng: School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, China
Yi Liu: School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, China

Energies, 2015, vol. 8, issue 12, 1-19

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.

Keywords: distributed generation; optimal allocation; crisscross optimization algorithm; Monte Carlo simulation; uncertainties (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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