Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids

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


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.


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



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.


  1. 1.
    Office of Electricity Delivery and Energy Reliability (2012) DOE microgrid workshop report. Summary ReportGoogle Scholar
  2. 2.
    Ton DT, Smith MA (2012) The U.S. Department of Energy’s microgrid initiative. Electr J 25(8):84–94Google Scholar
  3. 3.
    Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids for distributed generation. IEEE Power and Energy MagazineGoogle Scholar
  4. 4.
    Liu CC, McAuthur S, Lee SJ (2016) Smart grid handbook. In: 3 Volume Set. WileyGoogle Scholar
  5. 5.
    Investigating R&D Committee on advanced power system (2011) Current status of advanced power systems including microgrid and smartgrid (in Japanese). IEEJ Technical Report 1229Google Scholar
  6. 6.
    New Energy and Industrial Technology Development Organization (2018) Case Studies of Smart Community Demonstration Project. Access date: 31 May 2019
  7. 7.
    Kerr RH, Scheidt JL, Fontana AJ, Wiley JK (1966) Unit Commitment. IEEE Trans Power App Syst. PAS-85:417–421Google Scholar
  8. 8.
    Sen S, Kothari DP (1989) Optimal thermal generating unit commitment: a review. Int J Electr Power Energy Syst 20(7):443–451CrossRefGoogle Scholar
  9. 9.
    Hobbs BF, Rothkopf MH, O’Neill RP, Chao HP (2001) The next generation of electric power unit commitment models. In: International series in operations research & management science, vol 36Google Scholar
  10. 10.
    Padhy NP (2004) Unit commitment—a bibliographical survey. IEEE Trans Power Syst 19(2):1196–1205MathSciNetCrossRefGoogle Scholar
  11. 11.
    Bhardwaj A, Tung NS, Kamboj V (2012) Unit commitment in power system: a review. Int J Power Eng 6(1):51–57CrossRefGoogle Scholar
  12. 12.
    Saravanan B, Das S, Sikri S, Kothari DP (2013) A solution to the unit commitment problem—a review. Front Energy 7(2):223–236CrossRefGoogle Scholar
  13. 13.
    Zheng QP, Wang J, Liu AL (2015) Stochastic optimization for unit commitment—a review. IEEE Trans Power Syst 30(4):1913–1924CrossRefGoogle Scholar
  14. 14.
    Snyder WL, Powell HD, Raiburn JC (1987) Dynamic programming approach to unit commitment. IEEE Trans Power Syst 2(2):339–348CrossRefGoogle Scholar
  15. 15.
    Ouyang Z, Shahidehpour SM (1991) An intelligent dynamic programming for unit commitment application. IEEE Trans Power Syst 6(3):1203–1209CrossRefGoogle Scholar
  16. 16.
    Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power App Syst PAS-102(2):444–451Google Scholar
  17. 17.
    Chen CL, Wang SC (1993) Branch-and-bound scheduling for thermal generating units. IEEE Trans Energy Conversion 8(2):184–189CrossRefGoogle Scholar
  18. 18.
    Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92CrossRefGoogle Scholar
  19. 19.
    Mantawy AH, Abdel-Magid YL, Selim SZ (1998) A simulated annealing algorithm for unit commitment. IEEE Proc Generation Trans Distribution 145(1):56–64Google Scholar
  20. 20.
    Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithms. IEEE Trans Power Syst 21(1):68–76CrossRefGoogle Scholar
  21. 21.
    Takano H, Zhang P, Murata J, Hashiguchi T, Goda T, Iizaka T, Nakanishi Y (2015) A determination method for the optimal operation of controllable generators in micro grids that copes with unstable outputs of renewable energy generation. Electr Eng Japan 190(4):56–65CrossRefGoogle Scholar
  22. 22.
    Jeong YW, Park JB (2010) A new quantum-inspired binary PSO: application to unit commitment problem for power systems. IEEE Trans Power Syst 25(3):1486–1495CrossRefGoogle Scholar
  23. 23.
    Hayashi Y, Miyamoto H, Matsuki J, Iizuka T, Azuma H (2008) Online optimization method for operation of generators in micro Grid (in Japanese). IEEJ Trans PE128-B(2):388–396Google Scholar
  24. 24.
    Juste KA, Kita H, Tanaka E, Hasegawa J (1999) An evolutionary programming solution to the unit commitment problem. IEEE Trans Power Syst 14(4):1452–1459CrossRefGoogle Scholar
  25. 25.
    Rajan CCA, Mohan MR (2004) An evolutionary programming-based tabu search method for solving the unit commitment problem. IEEE Trans Power Syst 19(1):577–585CrossRefGoogle Scholar
  26. 26.
    Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. IEEE Trans PES 20(2):1053–1061Google Scholar
  27. 27.
    Palma-Behnke R, Benavides C, Lanas F, Severino B, Reyes L, Llanos J, Saez D (2013) A microgrid energy management system based on the rolling horizon strategy. IEEE Trans Smart Grid 4(2):996–1006CrossRefGoogle Scholar
  28. 28.
    Li N, Uckun C, Constantinescu EM, Birge JR, Hedman KW, Botterud A (2016) Flexible operation of batteries in power system scheduling with renewable energy. IEEE Trans Sustain Energy 7(2):685–696CrossRefGoogle Scholar
  29. 29.
    Hammati R, Saboori H (2016) Short-term bulk energy storage scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis. J Adv Res 7(3):360–372CrossRefGoogle Scholar
  30. 30.
    Soe TZ, Takano H, Shiomi R, Taoka H (2018) Determination method for optimal cooperative operation plan of microgrids by providing alternatives for microgrid operators. J Int Council Electr Eng 8(1):103–110Google Scholar
  31. 31.
    Takano H, Nagaki Y, Murata J, Iizaka T, Ishibashi T, Katsuno T (2016) A study on supply and demand planning for Power Producer-Suppliers utilizing output of megawatt solar plants. J Int Council Electr Eng 6(1):102–109Google Scholar
  32. 32.
    Clerc M (2006) Particle swarm optimization. ISTE LtdGoogle Scholar
  33. 33.
    Lee S, Soak S, Oh S, Pedryczm W, Jeon M (2008) Modified binary particle swarm optimization. Progress Natural Sci (18):1161–1166Google Scholar

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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