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An Improved DA-PSO Optimization Approach for Unit Commitment Problem

Author

Listed:
  • Sirote Khunkitti

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Neville R. Watson

    (Department of Electrical and Computer Engineering, University of Canterbury, Christchurch 8140, New Zealand)

  • Rongrit Chatthaworn

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Suttichai Premrudeepreechacharn

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Apirat Siritaratiwat

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

Abstract

Solving the Unit Commitment problem is an important step in optimally dispatching the available generation and involves two stages—deciding which generators to commit, and then deciding their power output (economic dispatch). The Unit Commitment problem is a mixed-integer combinational optimization problem that traditional optimization techniques struggle to solve, and metaheuristic techniques are better suited. Dragonfly algorithm (DA) and particle swarm optimization (PSO) are two such metaheuristic techniques, and recently a hybrid (DA-PSO), to make use of the best features of both, has been proposed. The original DA-PSO optimization is unable to solve the Unit Commitment problem because this is a mixed-integer optimization problem. However, this paper proposes a new and improved DA-PSO optimization (referred to as iDA-PSO) for solving the unit commitment and economic dispatch problems. The iDA-PSO employs a sigmoid function to find the optimal on/off status of units, which is the mixed-integer part of obtaining the Unit Commitment problem. To verify the effectiveness of the iDA-PSO approach, it was tested on four different-sized systems (5-unit, 6-unit, 10-unit, and 26-unit systems). The unit commitment, generation schedule, total generation cost, and time were compared with those obtained by other algorithms in the literature. The simulation results show iDA-PSO is a promising technique and is superior to many other algorithms in the literature.

Suggested Citation

  • Sirote Khunkitti & Neville R. Watson & Rongrit Chatthaworn & Suttichai Premrudeepreechacharn & Apirat Siritaratiwat, 2019. "An Improved DA-PSO Optimization Approach for Unit Commitment Problem," Energies, MDPI, vol. 12(12), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2335-:d:240940
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    References listed on IDEAS

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    1. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn & Rongrit Chatthaworn & Neville R. Watson, 2018. "A Hybrid DA-PSO Optimization Algorithm for Multiobjective Optimal Power Flow Problems," Energies, MDPI, vol. 11(9), pages 1-21, August.
    2. Ana Viana & Jorge de Sousa & Manuel Matos, 2003. "Using GRASP to Solve the Unit Commitment Problem," Annals of Operations Research, Springer, vol. 120(1), pages 117-132, April.
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    4. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher & Alizadeh Pahlavani, Mohammad Reza, 2011. "Multi-objective operation management of a renewable MG (micro-grid) with back-up micro-turbine/fuel cell/battery hybrid power source," Energy, Elsevier, vol. 36(11), pages 6490-6507.
    5. Moghaddam, Amjad Anvari & Seifi, Alireza & Niknam, Taher, 2012. "Multi-operation management of a typical micro-grids using Particle Swarm Optimization: A comparative study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1268-1281.
    6. Fatma Yaprakdal & Mustafa Baysal & Amjad Anvari-Moghaddam, 2019. "Optimal Operational Scheduling of Reconfigurable Microgrids in Presence of Renewable Energy Sources," Energies, MDPI, vol. 12(10), pages 1-17, May.
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    Cited by:

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    2. Mohammed A. El-Shorbagy & Hala A. Omar & Tamer Fetouh, 2022. "Hybridization of Manta-Ray Foraging Optimization Algorithm with Pseudo Parameter-Based Genetic Algorithm for Dealing Optimization Problems and Unit Commitment Problem," Mathematics, MDPI, vol. 10(13), pages 1-33, June.
    3. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    4. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
    5. Harun Or Rashid Howlader & Oludamilare Bode Adewuyi & Ying-Yi Hong & Paras Mandal & Ashraf Mohamed Hemeida & Tomonobu Senjyu, 2019. "Energy Storage System Analysis Review for Optimal Unit Commitment," Energies, MDPI, vol. 13(1), pages 1-21, December.

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