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Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids

  • Hirotaka TakanoEmail author
  • Hiroshi Asano
  • Neeraj Gupta
Chapter
  • 18 Downloads
Part of the Springer Tracts in Nature-Inspired Computing book series (STNIC)

Abstract

This chapter introduces problem frameworks to determine coordinated operation schedules of microgrid components including controllable generation systems (CGs), energy storage systems (ESSs) and controllable loads (CLs). The aim of this study is to design a profitable and stable operation of microgrids based on optimization theory and methods, and it has been attracting significant attention in the electric power field. Discussions of the problem frameworks include electricity trade with the conventional power grids and uncertainty originated from variable renewable energy sources and/or electric consumption. As the basis of solution method, particle swarm optimization (PSO), which is one of the most popular nature-inspired metaheuristic algorithms, is selected. In addition, with a view to improving compatibility of the problem frameworks and the solution methods, the authors transform the target optimization problems into lower dimensional problems. By this strategy, binary particle swarm optimization (BPSO) is applicable in corporation with quadratic programming (QP). Through numerical simulations on a typical microgrid model, validity of the problem frameworks and usefulness of the PSO-based solution methods are verified.

Keywords

Binary particle swarm optimization Quadratic programming Mixed-integer programming problems Unit commitment Economic load dispatch Microgrids Operation schedule Uncertainty 

Notes

Acknowledgements

The authors would like to acknowledge the support provided by Japan Society for the Promotion of Science (KAKENHI Grant Numbers 16K06215 and 19K04325) and Gifu Renewable Energy System Research Center of Gifu University. Contributions to this study by Ryota Goto and Kan Nakae, who are pursuing their master’s degree in Gifu University, are also acknowledged.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Electrical, Electronic and Computer EngineeringGifu UniversityGifu-Shi, GifuJapan
  2. 2.Energy Innovation CenterCentral Research Institute of Electric Power IndustryYokosuka-Shi, KanagawaJapan
  3. 3.Department of Computer Science and EngineeringOakland UniversityRochesterUSA

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