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Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization

Author

Listed:
  • Heba-Allah I. ElAzab

    (Faculty of Engineering, Ahram Canadian University(ACU), Giza 12573, Egypt)

  • R. A. Swief

    (Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Noha H. El-Amary

    (Arab Academy for Science, Technology and Maritime Transport (AASTMT), Cairo 2033, Egypt)

  • H. K. Temraz

    (Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

Abstract

This paper presents a trustworthy unit commitment study to schedule both Renewable Energy Resources (RERs) with conventional power plants to potentially decarbonize the electrical network. The study has employed a system with three IEEE thermal (coal-fired) power plants as dispatchable distributed generators, one wind plant, one solar plant as stochastic distributed generators, and Plug-in Electric Vehicles (PEVs) which can work either loads or generators based on their charging schedule. This paper investigates the unit commitment scheduling objective to minimize the Combined Economic Emission Dispatch (CEED). To reduce combined emission costs, integrating more renewable energy resources (RER) and PEVs, there is an essential need to decarbonize the existing system. Decarbonizing the system means reducing the percentage of CO 2 emissions. The uncertain behavior of wind and solar energies causes imbalance penalty costs. PEVs are proposed to overcome the intermittent nature of wind and solar energies. It is important to optimally integrate and schedule stochastic resources including the wind and solar energies, and PEVs charge and discharge processes with dispatched resources; the three IEEE thermal (coal-fired) power plants. The Water Cycle Optimization Algorithm (WCOA) is an efficient and intelligent meta-heuristic technique employed to solve the economically emission dispatch problem for both scheduling dispatchable and stochastic resources. The goal of this study is to obtain the solution for unit commitment to minimize the combined cost function including CO 2 emission costs applying the Water Cycle Optimization Algorithm (WCOA). To validate the WCOA technique, the results are compared with the results obtained from applying the Dynamic Programming (DP) algorithm, which is considered as a conventional numerical technique, and with the Genetic Algorithm (GA) as a meta-heuristic technique.

Suggested Citation

  • Heba-Allah I. ElAzab & R. A. Swief & Noha H. El-Amary & H. K. Temraz, 2018. "Unit Commitment Towards Decarbonized Network Facing Fixed and Stochastic Resources Applying Water Cycle Optimization," Energies, MDPI, vol. 11(5), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1140-:d:144457
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    References listed on IDEAS

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    1. Vasiliki Vita, 2017. "Development of a Decision-Making Algorithm for the Optimum Size and Placement of Distributed Generation Units in Distribution Networks," Energies, MDPI, vol. 10(9), pages 1-13, September.
    2. Björn Nykvist & Måns Nilsson, 2015. "Rapidly falling costs of battery packs for electric vehicles," Nature Climate Change, Nature, vol. 5(4), pages 329-332, April.
    3. Ghasemi, Ahmad & Mortazavi, Seyed Saeidollah & Mashhour, Elaheh, 2016. "Hourly demand response and battery energy storage for imbalance reduction of smart distribution company embedded with electric vehicles and wind farms," Renewable Energy, Elsevier, vol. 85(C), pages 124-136.
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    Cited by:

    1. Heba-Allah I. ElAzab & R. A. Swief & Hanady H. Issa & Noha H. El-Amary & Alsnosy Balbaa & H. K. Temraz, 2018. "FPGA Eco Unit Commitment Based Gravitational Search Algorithm Integrating Plug-in Electric Vehicles," Energies, MDPI, vol. 11(10), pages 1-17, September.
    2. Alsnosy Balbaa & R. A. Swief & Noha H. El-Amary, 2019. "Smart Integration Based on Hybrid Particle Swarm Optimization Technique for Carbon Dioxide Emission Reduction in Eco-Ports," Sustainability, MDPI, vol. 11(8), pages 1-16, April.
    3. Jacek Kropiwnicki & Mariusz Furmanek & Andrzej Rogala, 2021. "Modular Approach for Modelling Warming up Process in Water Installations with Flow-Regulating Elements," Energies, MDPI, vol. 14(15), pages 1-17, July.
    4. Xiaohui Yang & Jiating Long & Peiyun Liu & Xiaolong Zhang & Xiaoping Liu, 2018. "Optimal Scheduling of Microgrid with Distributed Power Based on Water Cycle Algorithm," Energies, MDPI, vol. 11(9), pages 1-17, September.
    5. 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.
    6. Rajitha Udawalpola & Taisuke Masuta & Taisei Yoshioka & Kohei Takahashi & Hideaki Ohtake, 2021. "Reduction of Power Imbalances Using Battery Energy Storage System in a Bulk Power System with Extremely Large Photovoltaics Interactions," Energies, MDPI, vol. 14(3), pages 1-27, January.
    7. Alaa Farah & Hamdy Hassan & Alaaeldin M. Abdelshafy & Abdelfatah M. Mohamed, 2020. "Optimal Scheduling of Hybrid Multi-Carrier System Feeding Electrical/Thermal Load Based on Particle Swarm Algorithm," Sustainability, MDPI, vol. 12(11), pages 1-21, June.

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