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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Office of Electricity Delivery and Energy Reliability (2012) DOE microgrid workshop report. Summary Report
Ton DT, Smith MA (2012) The U.S. Department of Energy’s microgrid initiative. Electr J 25(8):84–94
Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids for distributed generation. IEEE Power and Energy Magazine
Liu CC, McAuthur S, Lee SJ (2016) Smart grid handbook. In: 3 Volume Set. Wiley
Investigating R&D Committee on advanced power system (2011) Current status of advanced power systems including microgrid and smartgrid (in Japanese). IEEJ Technical Report 1229
New Energy and Industrial Technology Development Organization (2018) Case Studies of Smart Community Demonstration Project. http://www.nedo.go.jp/english/reports_20130222.html. Access date: 31 May 2019
Kerr RH, Scheidt JL, Fontana AJ, Wiley JK (1966) Unit Commitment. IEEE Trans Power App Syst. PAS-85:417–421
Sen S, Kothari DP (1989) Optimal thermal generating unit commitment: a review. Int J Electr Power Energy Syst 20(7):443–451
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 36
Padhy NP (2004) Unit commitment—a bibliographical survey. IEEE Trans Power Syst 19(2):1196–1205
Bhardwaj A, Tung NS, Kamboj V (2012) Unit commitment in power system: a review. Int J Power Eng 6(1):51–57
Saravanan B, Das S, Sikri S, Kothari DP (2013) A solution to the unit commitment problem—a review. Front Energy 7(2):223–236
Zheng QP, Wang J, Liu AL (2015) Stochastic optimization for unit commitment—a review. IEEE Trans Power Syst 30(4):1913–1924
Snyder WL, Powell HD, Raiburn JC (1987) Dynamic programming approach to unit commitment. IEEE Trans Power Syst 2(2):339–348
Ouyang Z, Shahidehpour SM (1991) An intelligent dynamic programming for unit commitment application. IEEE Trans Power Syst 6(3):1203–1209
Cohen AI, Yoshimura M (1983) A branch-and-bound algorithm for unit commitment. IEEE Trans Power App Syst PAS-102(2):444–451
Chen CL, Wang SC (1993) Branch-and-bound scheduling for thermal generating units. IEEE Trans Energy Conversion 8(2):184–189
Kazarlis SA, Bakirtzis AG, Petridis V (1996) A genetic algorithm solution to the unit commitment problem. IEEE Trans Power Syst 11(1):83–92
Mantawy AH, Abdel-Magid YL, Selim SZ (1998) A simulated annealing algorithm for unit commitment. IEEE Proc Generation Trans Distribution 145(1):56–64
Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithms. IEEE Trans Power Syst 21(1):68–76
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):56–65
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):1486–1495
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):388–396
Juste KA, Kita H, Tanaka E, Hasegawa J (1999) An evolutionary programming solution to the unit commitment problem. IEEE Trans Power Syst 14(4):1452–1459
Rajan CCA, Mohan MR (2004) An evolutionary programming-based tabu search method for solving the unit commitment problem. IEEE Trans Power Syst 19(1):577–585
Lu B, Shahidehpour M (2005) Short-term scheduling of battery in a grid-connected PV/battery system. IEEE Trans PES 20(2):1053–1061
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):996–1006
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):685–696
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):360–372
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):103–110
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):102–109
Clerc M (2006) Particle swarm optimization. ISTE Ltd
Lee S, Soak S, Oh S, Pedryczm W, Jeon M (2008) Modified binary particle swarm optimization. Progress Natural Sci (18):1161–1166
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Takano, H., Asano, H., Gupta, N. (2020). Application Example of Particle Swarm Optimization on Operation Scheduling of Microgrids. In: Khosravy, M., Gupta, N., Patel, N., Senjyu, T. (eds) Frontier Applications of Nature Inspired Computation. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-2133-1_10
Download citation
DOI: https://doi.org/10.1007/978-981-15-2133-1_10
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2132-4
Online ISBN: 978-981-15-2133-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)