IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6838-d1248930.html
   My bibliography  Save this article

Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty

Author

Listed:
  • Mark Kipngetich Kiptoo

    (Graduate School of Science and Engineering, University of the Ryukyus, Nishihara 903-0213, Japan)

  • Oludamilare Bode Adewuyi

    (Faculty of Engineering, Information and Systems, University of Tsukuba, 1 Chome-1-1 Tennodai, Ibaraki 305-8577, Japan)

  • Masahiro Furukakoi

    (National Institute of Technology, Sasebo College, Sasebo 857-1193, Japan)

  • Paras Mandal

    (Department of Electrical and Computer Engineering, University of Texas at El Paso, El Paso, TX 79968, USA)

  • Tomonobu Senjyu

    (Graduate School of Science and Engineering, University of the Ryukyus, Nishihara 903-0213, Japan)

Abstract

Weather-driven uncertainties and other extreme events, particularly with the increasing reliance on variable renewable energy (VRE), have made achieving a reliable microgrid operation increasingly challenging. This research proposes a comprehensive and integrated planning strategy for capacity sizing and operational planning, incorporating forecasting and demand response program (DRP) strategies to address microgrid operation under various conditions, accounting for uncertainties. The microgrid includes photovoltaic systems, wind turbines, and battery energy storage. Uncertainties in VREs and load fluctuations are modeled using Monte Carlo simulations (MCSs), while forecasting is based on the long short-term memory (LSTM) model. To determine the best techno-economic planning approach, six cases are formulated and solved using a multi-objective particle swarm optimization with multi-criteria ranking for these three objectives: total lifecycle costs (TLCC), reliability criteria, and surplus VRE curtailment. Shortage/surplus adaptive pricing combined with variable peak critical peak pricing (SSAP VP-CPP) DRP is devised and compared with a time-of-use VP-CPP DRP in mitigating the impacts of both critical and non-critical events in the system. The simulation results show that the integrated planning, which combines LSTM forecasting with DRP strategies, achieved about 7% and 5% TLCC reductions for deterministic and stochastic approaches, respectively. The approach allowed optimal sizing and operation planning, improving the utilization of VREs and effectively managing uncertainty, resulting in the most cost-effective and robust VRE-based microgrid with enhanced resilience and reliability.

Suggested Citation

  • Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Masahiro Furukakoi & Paras Mandal & Tomonobu Senjyu, 2023. "Integrated Multi-Criteria Planning for Resilient Renewable Energy-Based Microgrid Considering Advanced Demand Response and Uncertainty," Energies, MDPI, vol. 16(19), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6838-:d:1248930
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6838/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6838/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Innocent Kamwa & Leila Bagherzadeh & Atieh Delavari, 2023. "Integrated Demand Response Programs in Energy Hubs: A Review of Applications, Classifications, Models and Future Directions," Energies, MDPI, vol. 16(11), pages 1-21, May.
    2. Abdul Conteh & Mohammed Elsayed Lotfy & Oludamilare Bode Adewuyi & Paras Mandal & Hiroshi Takahashi & Tomonobu Senjyu, 2020. "Demand Response Economic Assessment with the Integration of Renewable Energy for Developing Electricity Markets," Sustainability, MDPI, vol. 12(7), pages 1-20, March.
    3. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    4. Singh, Bharat & Kumar, Ashwani, 2023. "Optimal energy management and feasibility analysis of hybrid renewable energy sources with BESS and impact of electric vehicle load with demand response program," Energy, Elsevier, vol. 278(PA).
    5. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    6. Park, Myoung-Ju & Ham, Andy, 2022. "Energy-aware flexible job shop scheduling under time-of-use pricing," International Journal of Production Economics, Elsevier, vol. 248(C).
    7. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    8. Mohammad Masih Sediqi & Mohammed Elsayed Lotfy & Abdul Matin Ibrahimi & Tomonobu Senjyu & Narayanan. K, 2019. "Stochastic Unit Commitment and Optimal Power Trading Incorporating PV Uncertainty," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    9. Furukakoi, Masahiro & Adewuyi, Oludamilare Bode & Matayoshi, Hidehito & Howlader, Abdul Motin & Senjyu, Tomonobu, 2018. "Multi objective unit commitment with voltage stability and PV uncertainty," Applied Energy, Elsevier, vol. 228(C), pages 618-623.
    10. Yang, Jun & Su, Changqi, 2021. "Robust optimization of microgrid based on renewable distributed power generation and load demand uncertainty," Energy, Elsevier, vol. 223(C).
    11. Máximo Méndez & Mariano Frutos & Fabio Miguel & Ricardo Aguasca-Colomo, 2020. "TOPSIS Decision on Approximate Pareto Fronts by Using Evolutionary Algorithms: Application to an Engineering Design Problem," Mathematics, MDPI, vol. 8(11), pages 1-27, November.
    12. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Harun Or Rashid Howlader & Akito Nakadomari & Tomonobu Senjyu, 2023. "Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies," Energies, MDPI, vol. 16(10), pages 1-25, May.
    13. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Mohammed Elsayed Lotfy & Theophilus Amara & Keifa Vamba Konneh & Tomonobu Senjyu, 2019. "Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning," Future Internet, MDPI, vol. 11(10), pages 1-16, October.
    14. Muhammad Khalid, 2019. "A Review on the Selected Applications of Battery-Supercapacitor Hybrid Energy Storage Systems for Microgrids," Energies, MDPI, vol. 12(23), pages 1-34, November.
    15. Zhang, Yagang & Zhao, Yunpeng & Shen, Xiaoyu & Zhang, Jinghui, 2022. "A comprehensive wind speed prediction system based on Monte Carlo and artificial intelligence algorithms," Applied Energy, Elsevier, vol. 305(C).
    16. Djamila Rekioua, 2023. "Energy Storage Systems for Photovoltaic and Wind Systems: A Review," Energies, MDPI, vol. 16(9), pages 1-26, May.
    Full references (including those not matched with items on IDEAS)

    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. Mark Kipngetich Kiptoo & Oludamilare Bode Adewuyi & Harun Or Rashid Howlader & Akito Nakadomari & Tomonobu Senjyu, 2023. "Optimal Capacity and Operational Planning for Renewable Energy-Based Microgrid Considering Different Demand-Side Management Strategies," Energies, MDPI, vol. 16(10), pages 1-25, May.
    2. Hu, Likun & Cao, Yi & Yin, Linfei, 2024. "Fractional-order long-term price guidance mechanism based on bidirectional prediction with attention mechanism for electric vehicle charging," Energy, Elsevier, vol. 293(C).
    3. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    4. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    5. Jiang, Sufan & Gao, Shan & Pan, Guangsheng & Zhao, Xin & Liu, Yu & Guo, Yasen & Wang, Sicheng, 2020. "A novel robust security constrained unit commitment model considering HVDC regulation," Applied Energy, Elsevier, vol. 278(C).
    6. Sun, Zeyi & Li, Lin & Bego, Andres & Dababneh, Fadwa, 2015. "Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system," International Journal of Production Economics, Elsevier, vol. 165(C), pages 112-119.
    7. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    8. Li, Xiao Hui & Hong, Seung Ho, 2014. "User-expected price-based demand response algorithm for a home-to-grid system," Energy, Elsevier, vol. 64(C), pages 437-449.
    9. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    10. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    11. Alagoz, B. Baykant & Kaygusuz, Asim & Akcin, Murat & Alagoz, Serkan, 2013. "A closed-loop energy price controlling method for real-time energy balancing in a smart grid energy market," Energy, Elsevier, vol. 59(C), pages 95-104.
    12. M. M. Hasan & Shakhawat Hossain & M. Mofijur & Zobaidul Kabir & Irfan Anjum Badruddin & T. M. Yunus Khan & Esam Jassim, 2023. "Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions," Energies, MDPI, vol. 16(18), pages 1-30, September.
    13. Diaa Salman & Mehmet Kusaf, 2021. "Short-Term Unit Commitment by Using Machine Learning to Cover the Uncertainty of Wind Power Forecasting," Sustainability, MDPI, vol. 13(24), pages 1-22, December.
    14. Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    15. Mohammad Masih Sediqi & Mohammed Elsayed Lotfy & Abdul Matin Ibrahimi & Tomonobu Senjyu & Narayanan. K, 2019. "Stochastic Unit Commitment and Optimal Power Trading Incorporating PV Uncertainty," Sustainability, MDPI, vol. 11(16), pages 1-16, August.
    16. Leila Bagherzadeh & Innocent Kamwa, 2023. "Joint Multi-Objective Allocation of Parking Lots and DERs in Active Distribution Network Considering Demand Response Programs," Energies, MDPI, vol. 16(23), pages 1-37, November.
    17. Biao Li & Pengfei Wang & Peng Sun & Rui Meng & Jun Zeng & Guanghui Liu, 2023. "A Model for Determining the Optimal Decommissioning Interval of Energy Equipment Based on the Whole Life Cycle Cost," Sustainability, MDPI, vol. 15(6), pages 1-28, March.
    18. Vaziri Rad, Mohammad Amin & Kasaeian, Alibakhsh & Niu, Xiaofeng & Zhang, Kai & Mahian, Omid, 2023. "Excess electricity problem in off-grid hybrid renewable energy systems: A comprehensive review from challenges to prevalent solutions," Renewable Energy, Elsevier, vol. 212(C), pages 538-560.
    19. Boukettaya, Ghada & Krichen, Lotfi, 2014. "A dynamic power management strategy of a grid connected hybrid generation system using wind, photovoltaic and Flywheel Energy Storage System in residential applications," Energy, Elsevier, vol. 71(C), pages 148-159.
    20. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).

    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:16:y:2023:i:19:p:6838-:d:1248930. 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.