Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem
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...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2015-12-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/8/12/12389 |
| _version_ | 1811304294086868992 |
|---|---|
| author | Xiangang Peng Lixiang Lin Weiqin Zheng Yi Liu |
| author_facet | Xiangang Peng Lixiang Lin Weiqin Zheng Yi Liu |
| author_sort | Xiangang Peng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:03:28Z |
| format | Article |
| id | doaj.art-6138417c87154af48957918cfa96f473 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T08:03:28Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-6138417c87154af48957918cfa96f4732022-12-22T02:55:13ZengMDPI AGEnergies1996-10732015-12-01812136411365910.3390/en81212389en81212389Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation ProblemXiangang Peng0Lixiang Lin1Weiqin Zheng2Yi Liu3School of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, ChinaSchool of Automation, Guangdong University of Technology, Lab 315, No.2, Laboratory Building, No.100, Higher Education Mega Center, Guangzhou 510006, ChinaDistributed 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.http://www.mdpi.com/1996-1073/8/12/12389distributed generationoptimal allocationcrisscross optimization algorithmMonte Carlo simulationuncertainties |
| spellingShingle | Xiangang Peng Lixiang Lin Weiqin Zheng Yi Liu Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem Energies distributed generation optimal allocation crisscross optimization algorithm Monte Carlo simulation uncertainties |
| title | Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem |
| title_full | Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem |
| title_fullStr | Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem |
| title_full_unstemmed | Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem |
| title_short | Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem |
| title_sort | crisscross optimization algorithm and monte carlo simulation for solving optimal distributed generation allocation problem |
| topic | distributed generation optimal allocation crisscross optimization algorithm Monte Carlo simulation uncertainties |
| url | http://www.mdpi.com/1996-1073/8/12/12389 |
| work_keys_str_mv | AT xiangangpeng crisscrossoptimizationalgorithmandmontecarlosimulationforsolvingoptimaldistributedgenerationallocationproblem AT lixianglin crisscrossoptimizationalgorithmandmontecarlosimulationforsolvingoptimaldistributedgenerationallocationproblem AT weiqinzheng crisscrossoptimizationalgorithmandmontecarlosimulationforsolvingoptimaldistributedgenerationallocationproblem AT yiliu crisscrossoptimizationalgorithmandmontecarlosimulationforsolvingoptimaldistributedgenerationallocationproblem |