Solving the Green Economic Load Dispatch by Applying the Novel Meta-heuristic Algorithm

Nguyen Anh Tang, Nguyen Minh Duc Cuong

Abstract


This study focuses on solving the green economic load dispatch problem by considering the presence of green energy sources, including wind energy and solar power plants. The main objective function of the whole study is to minimize the total fuel cost (TFC) of all the thermal generating sources (TGSs) in the system. Moreover, the multiple selection of all TGSs is also evaluated. Fire hawk optimization (FHO) and the Zebra optimization algorithm (ZOA) are applied to solve the problem of achieving the best TFC value and satisfying all the constraints involved. The results indicated that ZOA can achieve a better optimal solution compared to FHO. Particularly, the results obtained by ZOA are completely superior to FHO in all comparison criteria at two load demand levels, such as Best TFC value (Best.Cost), Average TFC value (Aver.Cost), and Maximum TFC value (Max.Cost). On top of that, ZOA is the only algorithm of two applied ones providing the fast convergence capability to the optimal value of the main objective functions in two cases of load demand levels. Therefore, ZOA is an efficient search method to deal with such GELD problems.

Keywords


Green economic load dispatch; Solar power plant; Wind power plant; Multiple fuel selection; Fire hawk optimization algorithm; Zebra optimization algorithm

Full Text:

PDF

References


K. Dasgupta and S. Banerjee, “An analysis of economic load dispatch using different algorithms,” in 2014 1st International Conference on Non Conventional Energy (ICONCE 2014), Jan. 2014, pp. 216–219. doi: 10.1109/ICONCE.2014.6808722.

T. T. Nguyen and D. N. Vo, “The application of one rank cuckoo search algorithm for solving economic load dispatch problems,” Appl. Soft Comput., vol. 37, pp. 763–773, Dec. 2015, doi: 10.1016/j.asoc.2015.09.010.

Q. H. Wu and J. T. Ma, “Power system optimal reactive power dispatch using evolutionary programming,” IEEE Trans. Power Syst., vol. 10, no. 3, pp. 1243–1249, 1995, doi: 10.1109/59.466531.

S. C. Lee and Y. H. Kim, “An enhanced Lagrangian neural network for the ELD problems with piecewise quadratic cost functions and nonlinear constraints,” Electr. Power Syst. Res., vol. 60, no. 3, pp. 167–177, Jan. 2002, doi: 10.1016/S0378-7796(01)00181-X.

J.-F. Chen, H.-C. Chen, and C.-L. Huang, “The uniqueness of the local minimum for power economic dispatch problems,” Electr. Power Syst. Res., vol. 32, no. 3, pp. 187–193, Mar. 1995, doi: 10.1016/0378-7796(94)00914-P.

N. Kherfane, R. L. Kherfane, M. Younes, and F. Khodja, “Economic and Emission Dispatch with Renewable Energy Using HSA,” Energy Procedia, vol. 50, pp. 970–979, 2014, doi: 10.1016/j.egypro.2014.06.116.

T. T. Nguyen, D. N. Vo, B. Dinhhoang, and L. H. Pham, “Modified Cuckoo Search Algorithm for Solving Nonconvex Economic Load Dispatch Problems,” Adv. Electr. Electron. Eng., vol. 14, no. 3, Sep. 2016, doi: 10.15598/aeee.v14i3.1633.

L. H. Pham, T. T. Nguyen, D. N. Vo, and C. D. Tran, “Adaptive Cuckoo Search Algorithm based Method for Economic Load Dispatch with Multiple Fuel Options and Valve Point Effect,” Int. J. Hybrid Inf. Technol., vol. 9, no. 1, pp. 41–50, Jan. 2016, doi: 10.14257/ijhit.2016.9.1.05.

V. Suresh and S. Sreejith, “Generation dispatch of combined solar thermal systems using dragonfly algorithm,” Computing, vol. 99, no. 1, pp. 59–80, Jan. 2017, doi: 10.1007/s00607-016-0514-9.

V. K. Jadoun, V. C. Pandey, N. Gupta, K. R. Niazi, and A. Swarnkar, “Integration of renewable energy sources in dynamic economic load dispatch problem using an improved fireworks algorithm,” IET Renew. Power Gener., vol. 12, no. 9, pp. 1004–1011, Jul. 2018, doi: 10.1049/iet-rpg.2017.0744.

J. C. Silva Chavez, A. Zamora-Mendez, M. R. Arrieta Paternina, J. F. Yrena Heredia, and R. Cardenas-Javier, “A hybrid optimization framework for the non-convex economic dispatch problem via meta-heuristic algorithms,” Electr. Power Syst. Res., vol. 177, p. 105999, Dec. 2019, doi: 10.1016/j.epsr.2019.105999.

S. Padhi, B. P. Panigrahi, and D. Dash, “Solving Dynamic Economic Emission Dispatch Problem with Uncertainty of Wind and Load Using Whale Optimization Algorithm,” J. Inst. Eng. Ser. B, vol. 101, no. 1, pp. 65–78, Feb. 2020, doi: 10.1007/s40031-020-00435-y.

Z.-F. Liu, L.-L. Li, Y.-W. Liu, J.-Q. Liu, H.-Y. Li, and Q. Shen, “Dynamic economic emission dispatch considering renewable energy generation: A novel multi-objective optimization approach,” Energy, vol. 235, p. 121407, Nov. 2021, doi: 10.1016/j.energy.2021.121407.

S. Hazra, T. Pal, and P. K. Roy, “Renewable Energy Based Economic Emission Load Dispatch Using Grasshopper Optimization Algorithm,” in Research Anthology on Clean Energy Management and Solutions, IGI Global, 2021, pp. 869–890. doi: 10.4018/978-1-7998-9152-9.ch036.

Y. Cao, T. Li, T. He, Y. Wei, M. Li, and F. Si, “Multiobjective Load Dispatch for Coal-Fired Power Plants under Renewable-Energy Accommodation Based on a Nondominated-Sorting Grey Wolf Optimizer Algorithm,” Energies, vol. 15, no. 8, p. 2915, Apr. 2022, doi: 10.3390/en15082915.

H. S. Hoang, V. B. Nguyen, V. D. Phan, and H. N. Nguyen, “Marine Predator Optimization Algorithm for Economic Load Dispatch Target Considering Solar Generators,” GMSARN Int. J., vol. 16, no. 1, pp. 11–26, 2022.

K. Nagarajan, A. Rajagopalan, S. Angalaeswari, L. Natrayan, and W. D. Mammo, “Combined Economic Emission Dispatch of Microgrid with the Incorporation of Renewable Energy Sources Using Improved Mayfly Optimization Algorithm,” Comput. Intell. Neurosci., vol. 2022, pp. 1–22, Apr. 2022, doi: 10.1155/2022/6461690.

I. Ahmed, M. Rehan, A. Basit, and K.-S. Hong, “Greenhouse gases emission reduction for electric power generation sector by efficient dispatching of thermal plants integrated with renewable systems,” Sci. Rep., vol. 12, no. 1, p. 12380, Jul. 2022, doi: 10.1038/s41598-022-15983-0.

J. Soni and K. Bhattacharjee, “Multi-objective dynamic economic emission dispatch integration with renewable energy sources and plug-in electrical vehicle using equilibrium optimizer,” Environ. Dev. Sustain., Mar. 2023, doi: 10.1007/s10668-023-03058-7.

S. Acharya, S. Ganesan, D. V. Kumar, and S. Subramanian, “Optimization of cost and emission for dynamic load dispatch problem with hybrid renewable energy sources,” Soft Comput., vol. 27, no. 20, pp. 14969–15001, Oct. 2023, doi: 10.1007/s00500-023-08584-0.

M. Azizi, S. Talatahari, and A. H. Gandomi, “Fire Hawk Optimizer: a novel metaheuristic algorithm,” Artif. Intell. Rev., vol. 56, no. 1, pp. 287–363, Jan. 2023, doi: 10.1007/s10462-022-10173-w.

M. Dehghani, S. Hubalovsky, and P. Trojovsky, “Tasmanian Devil Optimization: A New Bio-Inspired Optimization Algorithm for Solving Optimization Algorithm,” IEEE Access, vol. 10, pp. 19599–19620, 2022, doi: 10.1109/ACCESS.2022.3151641.

M. Abd Elaziz, A. Dahou, D. A. Orabi, S. Alshathri, E. M. Soliman, and A. A. Ewees, “A Hybrid Multitask Learning Framework with a Fire Hawk Optimizer for Arabic Fake News Detection,” Mathematics, vol. 11, no. 2, p. 258, Jan. 2023, doi: 10.3390/math11020258.

J. Nan, J. Wang, H. Wu, and K. Li, “Optimized Extreme Learning Machine by an Improved Harris Hawks Optimization Algorithm for Mine Fire Flame Recognition,” Mining, Metall. Explor., vol. 40, no. 1, pp. 367–388, Feb. 2023, doi: 10.1007/s42461-022-00719-5.

I. K. Gupta, A. K. Mishra, T. D. Diwan, and S. Srivastava, “Unequal clustering scheme for hotspot mitigation in IoT-enabled wireless sensor networks based on fire hawk optimization,” Comput. Electr. Eng., vol. 107, p. 108615, Apr. 2023, doi: 10.1016/j.compeleceng.2023.108615.

Y. Y. Ghadi, N. M. Neamah, A. A. Hossam-Eldin, M. Alqarni, and K. M. AboRas, “State-of-the-Art Frequency Control Strategy Based on an Optimal Fuzzy PI-FOPDF ? for SMES and UPFC Integrated Smart Grids Using Zebra Optimization Algorithm,” IEEE Access, vol. 11, pp. 122893–122910, 2023, doi: 10.1109/ACCESS.2023.3328961.

A. Rana, V. Khurana, A. Shrivastava, D. Gangodkar, D. Arora, and A. Kumar Dixit, “A ZEBRA Optimization Algorithm Search for Improving Localization in Wireless Sensor Network,” in 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), Oct. 2022, pp. 817–824. doi: 10.1109/ICTACS56270.2022.9988278.

P. Zare, I. F. Davoudkhani, R. Zare, H. Ghadimi, R. Mohajeri, and A. Babaei, “Maiden Application of Zebra Optimization Algorithm for Design PIDN-TIDF Controller for Frequency Control in Offshore Fixed Platforms Microgrid in the Presence of Tidal Energy,” in 2023 8th International Conference on Technology and Energy Management (ICTEM), Feb. 2023, pp. 1–7. doi: 10.1109/ICTEM56862.2023.10083612.




DOI: https://doi.org/10.33633/jcta.v1i2.9389

Article Metrics

Abstract view : 30 times
PDF - 20 times

Refbacks

  • There are currently no refbacks.


 IntSys Research futuretechsci.org

Journal of Computing Theories and Applications published by Dian Nuswantoro University, Semarang, Indonesia collaborates with the Intelligent System (IntSys) Research and is supported by Future Techno Science Foundation, Indonesia. 

Indexed by:

   

This journal is licensed under a Creative Commons Attribution 4.0 International License.

 

ISSN: 3024-9104