IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2335-d240940.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2335/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/12/2335/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Narimani, Mohammad Rasoul & Azizipanah-Abarghooee, Rasoul & Zoghdar-Moghadam-Shahrekohne, Behrouz & Gholami, Kayvan, 2013. "A novel approach to multi-objective optimal power flow by a new hybrid optimization algorithm considering generator constraints and multi-fuel type," Energy, Elsevier, vol. 49(C), pages 119-136.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Borche Postolov & Nikolay Hinov & Atanas Iliev & Dimitar Dimitrov, 2022. "Short-Term Hydro-Thermal-Solar Scheduling with CCGT Based on Self-Adaptive Genetic Algorithm," Energies, MDPI, vol. 15(16), pages 1-25, August.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Niknam, Taher & Azizipanah-Abarghooee, Rasoul & Narimani, Mohammad Rasoul, 2012. "An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation," Applied Energy, Elsevier, vol. 99(C), pages 455-470.
    2. Izadbakhsh, Maziar & Gandomkar, Majid & Rezvani, Alireza & Ahmadi, Abdollah, 2015. "Short-term resource scheduling of a renewable energy based micro grid," Renewable Energy, Elsevier, vol. 75(C), pages 598-606.
    3. Deihimi, Ali & Keshavarz Zahed, Babak & Iravani, Reza, 2016. "An interactive operation management of a micro-grid with multiple distributed generations using multi-objective uniform water cycle algorithm," Energy, Elsevier, vol. 106(C), pages 482-509.
    4. Sharma, Sharmistha & Bhattacharjee, Subhadeep & Bhattacharya, Aniruddha, 2018. "Probabilistic operation cost minimization of Micro-Grid," Energy, Elsevier, vol. 148(C), pages 1116-1139.
    5. Wang, Guibin & Zha, Yongxing & Wu, Ting & Qiu, Jing & Peng, Jian-chun & Xu, Gang, 2020. "Cross entropy optimization based on decomposition for multi-objective economic emission dispatch considering renewable energy generation uncertainties," Energy, Elsevier, vol. 193(C).
    6. Moradi, Mohammad H. & Eskandari, Mohsen, 2014. "A hybrid method for simultaneous optimization of DG capacity and operational strategy in microgrids considering uncertainty in electricity price forecasting," Renewable Energy, Elsevier, vol. 68(C), pages 697-714.
    7. Niknam, Taher & Golestaneh, Faranak & Shafiei, Mehdi, 2013. "Probabilistic energy management of a renewable microgrid with hydrogen storage using self-adaptive charge search algorithm," Energy, Elsevier, vol. 49(C), pages 252-267.
    8. 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.
    9. Whei-Min Lin & Chia-Sheng Tu & Ming-Tang Tsai, 2015. "Energy Management Strategy for Microgrids by Using Enhanced Bee Colony Optimization," Energies, MDPI, vol. 9(1), pages 1-16, December.
    10. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    11. Elsied, Moataz & Oukaour, Amrane & Gualous, Hamid & Hassan, Radwan, 2015. "Energy management and optimization in microgrid system based on green energy," Energy, Elsevier, vol. 84(C), pages 139-151.
    12. Haddadian, Hossein & Noroozian, Reza, 2017. "Optimal operation of active distribution systems based on microgrid structure," Renewable Energy, Elsevier, vol. 104(C), pages 197-210.
    13. Firouzmakan, Pouya & Hooshmand, Rahmat-Allah & Bornapour, Mosayeb & Khodabakhshian, Amin, 2019. "A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 355-368.
    14. Entchev, E. & Yang, L. & Ghorab, M. & Lee, E.J., 2013. "Simulation of hybrid renewable microgeneration systems in load sharing applications," Energy, Elsevier, vol. 50(C), pages 252-261.
    15. Konečná, Eva & Teng, Sin Yong & Máša, Vítězslav, 2020. "New insights into the potential of the gas microturbine in microgrids and industrial applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    16. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    17. Wang, Chengshan & Liu, Yixin & Li, Xialin & Guo, Li & Qiao, Lei & Lu, Hai, 2016. "Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system," Energy, Elsevier, vol. 97(C), pages 90-104.
    18. Zunaira Nadeem & Nadeem Javaid & Asad Waqar Malik & Sohail Iqbal, 2018. "Scheduling Appliances with GA, TLBO, FA, OSR and Their Hybrids Using Chance Constrained Optimization for Smart Homes," Energies, MDPI, vol. 11(4), pages 1-30, April.
    19. Shahenda Sarhan & Ragab El-Sehiemy & Amlak Abaza & Mona Gafar, 2022. "Turbulent Flow of Water-Based Optimization for Solving Multi-Objective Technical and Economic Aspects of Optimal Power Flow Problems," Mathematics, MDPI, vol. 10(12), pages 1-22, June.
    20. Aghajani, G.R. & Shayanfar, H.A. & Shayeghi, H., 2017. "Demand side management in a smart micro-grid in the presence of renewable generation and demand response," Energy, Elsevier, vol. 126(C), pages 622-637.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2335-:d:240940. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.