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Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm

Author

Listed:
  • Al-Attar Ali Mohamed

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Shimaa Ali

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

  • Salem Alkhalaf

    (Department of Computer Science, Alrass College of Science and Arts, Qassim University, Qassim, Arrass 51921, Saudi Arabia)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, Faculty of Engineering, University of the Ryukyus, Senbaru 9030213, Japan)

  • Ashraf M. Hemeida

    (Department of Electrical Engineering, Faculty of Energy Engineering, Aswan University, Aswan 81528, Egypt)

Abstract

This article offers a multi-objective framework for an optimal mix of different types of distributed energy resources (DERs) under different load models. Many renewable and non-renewable energy resources like photovoltaic system (PV), micro-turbine (MT), fuel cell (FC), and wind turbine system (WT) are incorporated in a grid-connected hybrid power system to supply energy demand. The main aim of this article is to maximize environmental, technical, and economic benefits by minimizing various objective functions such as the annual cost, power loss and greenhouse gas emission subject to different power system constraints and uncertainty of renewable energy sources. For each load model, optimum DER size and its corresponding location are calculated. To test the feasibility and validation of the multi-objective water cycle algorithm (MOWCA) is conducted on the IEEE-33 bus and IEEE-69 bus network. The concept of Pareto-optimality is applied to generate trilateral surface of non-dominant Pareto-optimal set followed by a fuzzy decision-making mechanism to obtain the final compromise solution. Multi-objective non-dominated sorting genetic (NSGA-III) algorithm is also implemented and the simulation results between two algorithms are compared with each other. The achieved simulation results evidence the better performance of MOWCA comparing with the NSGA-III algorithm and at different load models, the determined DER locations and size are always righteous for enhancement of the distribution power system performance parameters.

Suggested Citation

  • Al-Attar Ali Mohamed & Shimaa Ali & Salem Alkhalaf & Tomonobu Senjyu & Ashraf M. Hemeida, 2019. "Optimal Allocation of Hybrid Renewable Energy System by Multi-Objective Water Cycle Algorithm," Sustainability, MDPI, vol. 11(23), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6550-:d:289081
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    References listed on IDEAS

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    1. Aien, Morteza & Hajebrahimi, Ali & Fotuhi-Firuzabad, Mahmud, 2016. "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1077-1089.
    2. Vikas Singh Bhadoria & Nidhi Singh Pal & Vivek Shrivastava, 2019. "Artificial immune system based approach for size and location optimization of distributed generation in distribution system," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(3), pages 339-349, June.
    3. Li, Yang & Feng, Bo & Li, Guoqing & Qi, Junjian & Zhao, Dongbo & Mu, Yunfei, 2018. "Optimal distributed generation planning in active distribution networks considering integration of energy storage," Applied Energy, Elsevier, vol. 210(C), pages 1073-1081.
    4. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Review of hybrid renewable energy systems with comparative analysis of off-grid hybrid system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2217-2235.
    5. 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.
    6. Diemuodeke, E.O. & Addo, A. & Oko, C.O.C. & Mulugetta, Y. & Ojapah, M.M., 2019. "Optimal mapping of hybrid renewable energy systems for locations using multi-criteria decision-making algorithm," Renewable Energy, Elsevier, vol. 134(C), pages 461-477.
    7. Sanajaoba Singh, Sarangthem & Fernandez, Eugene, 2018. "Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system," Energy, Elsevier, vol. 143(C), pages 719-731.
    8. Guangqian, Du & Bekhrad, Kaveh & Azarikhah, Pouria & Maleki, Akbar, 2018. "A hybrid algorithm based optimization on modeling of grid independent biodiesel-based hybrid solar/wind systems," Renewable Energy, Elsevier, vol. 122(C), pages 551-560.
    9. Zhang, Weiping & Maleki, Akbar & Rosen, Marc A. & Liu, Jingqing, 2018. "Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage," Energy, Elsevier, vol. 163(C), pages 191-207.
    10. Chauhan, Anurag & Saini, R.P., 2016. "Discrete harmony search based size optimization of Integrated Renewable Energy System for remote rural areas of Uttarakhand state in India," Renewable Energy, Elsevier, vol. 94(C), pages 587-604.
    11. Nadjemi, O. & Nacer, T. & Hamidat, A. & Salhi, H., 2017. "Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1352-1365.
    12. Guo, Shaopeng & Liu, Qibin & Sun, Jie & Jin, Hongguang, 2018. "A review on the utilization of hybrid renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1121-1147.
    13. Esmaeili, Mobin & Sedighizadeh, Mostafa & Esmaili, Masoud, 2016. "Multi-objective optimal reconfiguration and DG (Distributed Generation) power allocation in distribution networks using Big Bang-Big Crunch algorithm considering load uncertainty," Energy, Elsevier, vol. 103(C), pages 86-99.
    14. Minh Quan Duong & Thai Dinh Pham & Thang Trung Nguyen & Anh Tuan Doan & Hai Van Tran, 2019. "Determination of Optimal Location and Sizing of Solar Photovoltaic Distribution Generation Units in Radial Distribution Systems," Energies, MDPI, vol. 12(1), pages 1-24, January.
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    3. Mahmoud G. Hemeida & Salem Alkhalaf & Al-Attar A. Mohamed & Abdalla Ahmed Ibrahim & Tomonobu Senjyu, 2020. "Distributed Generators Optimization Based on Multi-Objective Functions Using Manta Rays Foraging Optimization Algorithm (MRFO)," Energies, MDPI, vol. 13(15), pages 1-37, July.
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    5. Karar Mahmoud & Mohamed Abdel-Nasser & Eman Mustafa & Ziad M. Ali, 2020. "Improved Salp–Swarm Optimizer and Accurate Forecasting Model for Dynamic Economic Dispatch in Sustainable Power Systems," Sustainability, MDPI, vol. 12(2), pages 1-21, January.
    6. Kumar Shivam & Jong-Chyuan Tzou & Shang-Chen Wu, 2020. "Multi-Objective Sizing Optimization of a Grid-Connected Solar–Wind Hybrid System Using Climate Classification: A Case Study of Four Locations in Southern Taiwan," Energies, MDPI, vol. 13(10), pages 1-30, May.
    7. Ayat Ali Saleh & Tomonobu Senjyu & Salem Alkhalaf & Majed A. Alotaibi & Ashraf M. Hemeida, 2020. "Water Cycle Algorithm for Probabilistic Planning of Renewable Energy Resource, Considering Different Load Models," Energies, MDPI, vol. 13(21), pages 1-24, November.
    8. Xiaotong Guo & Lingyan Li & Haiyan Xie & Wei Shi, 2020. "Improved Multi-Objective Optimization Model for Policy Design of Rental Housing Market," Sustainability, MDPI, vol. 12(14), pages 1-23, July.

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