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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Xiangang Peng & Lixiang Lin & Weiqin Zheng & Yi Liu, 2015. "Crisscross Optimization Algorithm and Monte Carlo Simulation for Solving Optimal Distributed Generation Allocation Problem," Energies, MDPI, vol. 8(12), pages 1-19, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12389-13659:d:59739
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    References listed on IDEAS

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    4. Mahesh Kumar & Amir Mahmood Soomro & Waqar Uddin & Laveet Kumar, 2022. "Optimal Multi-Objective Placement and Sizing of Distributed Generation in Distribution System: A Comprehensive Review," Energies, MDPI, vol. 15(21), pages 1-48, October.
    5. Jingmin Fan & Huidong Shao & Yunfei Cao & Lutao Feng & Jianpei Chen & Anbo Meng & Hao Yin, 2022. "Condition Forecasting of a Power Transformer Based on an Online Monitor with EL-CSO-ANN," Energies, MDPI, vol. 15(22), pages 1-14, November.
    6. Mahesh Kumar & Perumal Nallagownden & Irraivan Elamvazuthi, 2017. "Optimal Placement and Sizing of Renewable Distributed Generations and Capacitor Banks into Radial Distribution Systems," Energies, MDPI, vol. 10(6), pages 1-25, June.
    7. Hamed Moazami Goodarzi & Mohammad Hosein Kazemi, 2017. "A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm," Energies, MDPI, vol. 10(4), pages 1-17, April.
    8. Ashraf Ramadan & Mohamed Ebeed & Salah Kamel & Almoataz Y. Abdelaziz & Hassan Haes Alhelou, 2021. "Scenario-Based Stochastic Framework for Optimal Planning of Distribution Systems Including Renewable-Based DG Units," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    9. Tang, Xiongmin & Li, Zhengshuo & Xu, Xuancong & Zeng, Zhijun & Jiang, Tianhong & Fang, Wenrui & Meng, Anbo, 2022. "Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm," Energy, Elsevier, vol. 244(PA).
    10. Calise, Francesco & de Notaristefani di Vastogirardi, Giulio & Dentice d'Accadia, Massimo & Vicidomini, Maria, 2018. "Simulation of polygeneration systems," Energy, Elsevier, vol. 163(C), pages 290-337.
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    12. Lei Yang & Xiaohui Yang & Yue Wu & Xiaoping Liu, 2018. "Applied Research on Distributed Generation Optimal Allocation Based on Improved Estimation of Distribution Algorithm," Energies, MDPI, vol. 11(9), pages 1-17, September.

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