IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i1p330-d717222.html
   My bibliography  Save this article

A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data

Author

Listed:
  • Md Masud Rana

    (Centre for Smart Grid Energy Research (CSMER), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

  • Akhlaqur Rahman

    (Department of Electrical Engineering and Industrial Automation, Melbourne Campus, Engineering Institute of Technology, Melbourne, VIC 3001, Australia)

  • Moslem Uddin

    (School of Engineering & Information Technology, The University of New South Wales, Canberra, ACT 2610, Australia)

  • Md Rasel Sarkar

    (School of Engineering & Information Technology, The University of New South Wales, Canberra, ACT 2610, Australia)

  • Sk. A. Shezan

    (Department of Electrical Engineering and Industrial Automation, Melbourne Campus, Engineering Institute of Technology, Melbourne, VIC 3001, Australia)

  • Md. Fatin Ishraque

    (Department of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh)

  • S M Sajjad Hossain Rafin

    (School of Engineering and Built Environment, Griffith University, Gold Coast, QLD 4215, Australia)

  • Mohamed Atef

    (Centre for Smart Grid Energy Research (CSMER), Electrical and Electronic Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Perak, Malaysia)

Abstract

Peak load reduction is one of the most essential obligations and cost-effective tasks for electrical energy consumers. An isolated microgrid (IMG) system is an independent limited capacity power system where the peak shaving application can perform a vital role in the economic operation. This paper presents a comparative analysis of a categorical variable decision tree algorithm (CVDTA) with the most common peak shaving technique, namely, the general capacity addition technique, to evaluate the peak shaving performance for an IMG system. The CVDTA algorithm deals with the hybrid photovoltaic (PV)—battery energy storage system (BESS) to provide the peak shaving service where the capacity addition technique uses a peaking generator to minimize the peak demand. An actual IMG system model is developed in MATLAB/Simulink software to analyze the peak shaving performance. The model consists of four major components such as, PV, BESS, variable load, and gas turbine generator (GTG) dispatch models for the proposed algorithm, where the BESS and PV models are not applicable for the capacity addition technique. Actual variable load data and PV generation data are considered to conduct the simulation case studies which are collected from a real IMG system. The simulation result exhibits the effectiveness of the CVDTA algorithm which can minimize the peak demand better than the capacity addition technique. By ensuring the peak shaving operation and handling the economic generation dispatch, the CVDTA algorithm can ensure more energy savings, fewer system losses, less operation and maintenance (O&M) cost, etc., where the general capacity addition technique is limited.

Suggested Citation

  • Md Masud Rana & Akhlaqur Rahman & Moslem Uddin & Md Rasel Sarkar & Sk. A. Shezan & Md. Fatin Ishraque & S M Sajjad Hossain Rafin & Mohamed Atef, 2022. "A Comparative Analysis of Peak Load Shaving Strategies for Isolated Microgrid Using Actual Data," Energies, MDPI, vol. 15(1), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:330-:d:717222
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/1/330/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/1/330/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhang, Yan & Fu, Lijun & Zhu, Wanlu & Bao, Xianqiang & Liu, Cang, 2018. "Robust model predictive control for optimal energy management of island microgrids with uncertainties," Energy, Elsevier, vol. 164(C), pages 1229-1241.
    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. Tee, Wei Hown & Gan, Chin Kim & Sardi, Junainah, 2024. "Benefits of energy storage systems and its potential applications in Malaysia: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    3. Sk. A. Shezan & Innocent Kamwa & Md. Fatin Ishraque & S. M. Muyeen & Kazi Nazmul Hasan & R. Saidur & Syed Muhammad Rizvi & Md Shafiullah & Fahad A. Al-Sulaiman, 2023. "Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    4. Md. Fatin Ishraque & Akhlaqur Rahman & Sk. A. Shezan & S. M. Muyeen, 2022. "Grid Connected Microgrid Optimization and Control for a Coastal Island in the Indian Ocean," Sustainability, MDPI, vol. 14(24), pages 1-22, December.
    5. Md Masud Rana & Akhlaqur Rahman & Moslem Uddin & Md Rasel Sarkar & SK. A. Shezan & C M F S Reza & Md. Fatin Ishraque & Mohammad Belayet Hossain, 2022. "Efficient Energy Distribution for Smart Household Applications," Energies, MDPI, vol. 15(6), pages 1-19, March.

    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. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    2. Peixiao Fan & Jia Hu & Song Ke & Yuxin Wen & Shaobo Yang & Jun Yang, 2022. "A Frequency–Pressure Cooperative Control Strategy of Multi-Microgrid with an Electric–Gas System Based on MADDPG," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    3. Golpîra, Hêriş & Khan, Syed Abdul Rehman, 2019. "A multi-objective risk-based robust optimization approach to energy management in smart residential buildings under combined demand and supply uncertainty," Energy, Elsevier, vol. 170(C), pages 1113-1129.
    4. Luo, Jianing & Li, Hangxin & Wang, Shengwei, 2022. "A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties," Applied Energy, Elsevier, vol. 328(C).
    5. Jiao, Feixiang & Zou, Yuan & Zhang, Xudong & Zhang, Bin, 2022. "Online optimal dispatch based on combined robust and stochastic model predictive control for a microgrid including EV charging station," Energy, Elsevier, vol. 247(C).
    6. Wan, Xin & Luo, Xiong-Lin, 2020. "Economic optimization of chemical processes based on zone predictive control with redundancy variables," Energy, Elsevier, vol. 212(C).
    7. Chuan Xiang & Qi Cheng & Yizheng Zhu & Hongge Zhao, 2023. "Sliding Mode Control of Ship DC Microgrid Based on an Improved Reaching Law," Energies, MDPI, vol. 16(3), pages 1-14, January.
    8. Aqdas Naz & Nadeem Javaid & Muhammad Babar Rasheed & Abdul Haseeb & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
    9. Bahramara, Salah & Sheikhahmadi, Pouria & Golpîra, Hêmin, 2019. "Co-optimization of energy and reserve in standalone micro-grid considering uncertainties," Energy, Elsevier, vol. 176(C), pages 792-804.
    10. Zheng, Lingwei & Zhou, Xingqiu & Qiu, Qi & Yang, Lan, 2020. "Day-ahead optimal dispatch of an integrated energy system considering time-frequency characteristics of renewable energy source output," Energy, Elsevier, vol. 209(C).
    11. Wei, Shangshang & Gao, Xianhua & Zhang, Yi & Li, Yiguo & Shen, Jiong & Li, Zuyi, 2021. "An improved stochastic model predictive control operation strategy of integrated energy system based on a single-layer multi-timescale framework," Energy, Elsevier, vol. 235(C).
    12. Hu, Mian & Wang, Yan-Wu & Xiao, Jiang-Wen & Lin, Xiangning, 2019. "Multi-energy management with hierarchical distributed multi-scale strategy for pelagic islanded microgrid clusters," Energy, Elsevier, vol. 185(C), pages 910-921.
    13. Jiao, Feixiang & Ji, Chengda & Zou, Yuan & Zhang, Xudong, 2021. "Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV," Applied Energy, Elsevier, vol. 304(C).
    14. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    15. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Santarelli, Marina, 2019. "Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance," Energy, Elsevier, vol. 172(C), pages 997-1004.
    16. Hu, Jiefeng & Shan, Yinghao & Guerrero, Josep M. & Ioinovici, Adrian & Chan, Ka Wing & Rodriguez, Jose, 2021. "Model predictive control of microgrids – An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    17. Zehra, Syeda Shafia & Ur Rahman, Aqeel & Ahmad, Iftikhar, 2022. "Fuzzy-barrier sliding mode control of electric-hydrogen hybrid energy storage system in DC microgrid: Modelling, management and experimental investigation," Energy, Elsevier, vol. 239(PD).
    18. Luis Gabriel Marín & Mark Sumner & Diego Muñoz-Carpintero & Daniel Köbrich & Seksak Pholboon & Doris Sáez & Alfredo Núñez, 2019. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control," Energies, MDPI, vol. 12(23), pages 1-19, November.
    19. Jithendranath, J. & Das, Debapriya & Guerrero, Josep M., 2021. "Probabilistic optimal power flow in islanded microgrids with load, wind and solar uncertainties including intermittent generation spatial correlation," Energy, Elsevier, vol. 222(C).
    20. Khan, Saad Salman & Ahmad, Sadiq & Naeem, Muhammad, 2023. "On-grid joint energy management and trading in uncertain environment," Applied Energy, Elsevier, vol. 330(PB).

    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:15:y:2022:i:1:p:330-:d:717222. 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.