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Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

Author

Listed:
  • Loau Al-Bahrani

    (School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Mehdi Seyedmahmoudian

    (School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

  • Ben Horan

    (School of Engineering, Deakin University, Waurn Ponds, VIC 3216, Australia)

  • Alex Stojcevski

    (School of Software and Electrical Engineering, Swinburne University of Technology, Hawthorn, VIC 3122, Australia)

Abstract

Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.

Suggested Citation

  • Loau Al-Bahrani & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2021. "Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations," Sustainability, MDPI, vol. 13(3), pages 1-26, January.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1274-:d:487027
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    References listed on IDEAS

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    1. Mohammadi-ivatloo, Behnam & Rabiee, Abbas & Soroudi, Alireza & Ehsan, Mehdi, 2012. "Imperialist competitive algorithm for solving non-convex dynamic economic power dispatch," Energy, Elsevier, vol. 44(1), pages 228-240.
    2. Zou, Dexuan & Li, Steven & Kong, Xiangyong & Ouyang, Haibin & Li, Zongyan, 2019. "Solving the combined heat and power economic dispatch problems by an improved genetic algorithm and a new constraint handling strategy," Applied Energy, Elsevier, vol. 237(C), pages 646-670.
    3. Zou, Dexuan & Li, Steven & Wang, Gai-Ge & Li, Zongyan & Ouyang, Haibin, 2016. "An improved differential evolution algorithm for the economic load dispatch problems with or without valve-point effects," Applied Energy, Elsevier, vol. 181(C), pages 375-390.
    4. McLarty, Dustin & Panossian, Nadia & Jabbari, Faryar & Traverso, Alberto, 2019. "Dynamic economic dispatch using complementary quadratic programming," Energy, Elsevier, vol. 166(C), pages 755-764.
    5. Roy, Sanjoy, 2018. "The maximum likelihood optima for an economic load dispatch in presence of demand and generation variability," Energy, Elsevier, vol. 147(C), pages 915-923.
    6. Elattar, Ehab E., 2019. "Environmental economic dispatch with heat optimization in the presence of renewable energy based on modified shuffle frog leaping algorithm," Energy, Elsevier, vol. 171(C), pages 256-269.
    7. Singh, Priyanka & Dwivedi, Pragya, 2019. "A novel hybrid model based on neural network and multi-objective optimization for effective load forecast," Energy, Elsevier, vol. 182(C), pages 606-622.
    8. Meng, Anbo & Hu, Hanwu & Yin, Hao & Peng, Xiangang & Guo, Zhuangzhi, 2015. "Crisscross optimization algorithm for large-scale dynamic economic dispatch problem with valve-point effects," Energy, Elsevier, vol. 93(P2), pages 2175-2190.
    9. Niknam, Taher & Mojarrad, Hassan Doagou & Nayeripour, Majid, 2010. "A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch," Energy, Elsevier, vol. 35(4), pages 1764-1778.
    10. Motaeb Eid Alshammari & Makbul A. M. Ramli & Ibrahim M. Mehedi, 2020. "An Elitist Multi-Objective Particle Swarm Optimization Algorithm for Sustainable Dynamic Economic Emission Dispatch Integrating Wind Farms," Sustainability, MDPI, vol. 12(18), pages 1-15, September.
    11. Huang, Yu-Xian & Wang, Xiao-Dong & Cheng, Chin-Hsiang & Lin, David Ta-Wei, 2013. "Geometry optimization of thermoelectric coolers using simplified conjugate-gradient method," Energy, Elsevier, vol. 59(C), pages 689-697.
    12. Al-Bahrani, Loau Tawfak & Chandra Patra, Jagdish, 2018. "Multi-gradient PSO algorithm for optimization of multimodal, discontinuous and non-convex fuel cost function of thermal generating units under various power constraints in smart power grid," Energy, Elsevier, vol. 147(C), pages 1070-1091.
    13. Singh, Diljinder & Dhillon, J.S., 2019. "Ameliorated grey wolf optimization for economic load dispatch problem," Energy, Elsevier, vol. 169(C), pages 398-419.
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    Cited by:

    1. Hu, Zhongbo & Dai, Canyun & Su, Qinghua, 2022. "Adaptive backtracking search optimization algorithm with a dual-learning strategy for dynamic economic dispatch with valve-point effects," Energy, Elsevier, vol. 248(C).
    2. Ragab El-Sehiemy & Abdullah Shaheen & Ahmed Ginidi & Mostafa Elhosseini, 2022. "A Honey Badger Optimization for Minimizing the Pollutant Environmental Emissions-Based Economic Dispatch Model Integrating Combined Heat and Power Units," Energies, MDPI, vol. 15(20), pages 1-22, October.
    3. Ghulam Abbas & Irfan Ahmad Khan & Naveed Ashraf & Muhammad Taskeen Raza & Muhammad Rashad & Raheel Muzzammel, 2023. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    4. Abdulrashid Muhammad Kabir & Mohsin Kamal & Fiaz Ahmad & Zahid Ullah & Fahad R. Albogamy & Ghulam Hafeez & Faizan Mehmood, 2021. "Optimized Economic Load Dispatch with Multiple Fuels and Valve-Point Effects Using Hybrid Genetic–Artificial Fish Swarm Algorithm," Sustainability, MDPI, vol. 13(19), pages 1-27, September.

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