IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v179y2021icp1271-1290.html
   My bibliography  Save this article

Assessing demand compliance and reliability in the Philippine off-grid islands with Model Predictive Control microgrid coordination

Author

Listed:
  • Morato, Marcelo M.
  • Vergara-Dietrich, José
  • Esparcia, Eugene A.
  • Ocon, Joey D.
  • Normey-Rico, Julio E.

Abstract

This paper considers off-grid microgrids (MGs) from the Philippine archipelago and analyses their energy generation in differents aspects. Seven different energy clusters are used, representing realistic configurations and renewable energy shares. A Robust Model Predictive Control (MPC) framework is used for the energy management and coordination task of these island MGs. The MPC is based on a min./max. optimization procedure, which takes into account the whole uncertainty set. The reliability of the MG operations are analysed with respect to the different clusters; this evaluation is conducted using μ-analysis, performed with respect to the baseline model and the uncertainty set. The demand-side compliance of the MG is also investigated, with respect to stochastic behaviours of the demands and of the renewable sources (wind and solar). Numerical simulation results are presented in order to demonstrate that reliable power outlets are produced despite variation in renewables and of the demands. This paper offers a thorough analysis of simple energy system coordinated via MPC, showing how this method can indeed be used for renewable MG management, offering robustness and ensuring reliability.

Suggested Citation

  • Morato, Marcelo M. & Vergara-Dietrich, José & Esparcia, Eugene A. & Ocon, Joey D. & Normey-Rico, Julio E., 2021. "Assessing demand compliance and reliability in the Philippine off-grid islands with Model Predictive Control microgrid coordination," Renewable Energy, Elsevier, vol. 179(C), pages 1271-1290.
    • Handle: RePEc:eee:renene:v:179:y:2021:i:c:p:1271-1290
    DOI: 10.1016/j.renene.2021.07.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812101017X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2021.07.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    2. Bertheau, Paul & Cader, Catherina, 2019. "Electricity sector planning for the Philippine islands: Considering centralized and decentralized supply options," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    4. Mengelkamp, Esther & Gärttner, Johannes & Rock, Kerstin & Kessler, Scott & Orsini, Lawrence & Weinhardt, Christof, 2018. "Designing microgrid energy markets," Applied Energy, Elsevier, vol. 210(C), pages 870-880.
    5. Meschede, Henning & Esparcia, Eugene A. & Holzapfel, Peter & Bertheau, Paul & Ang, Rosario C. & Blanco, Ariel C. & Ocon, Joey D., 2019. "On the transferability of smart energy systems on off-grid islands using cluster analysis – A case study for the Philippine archipelago," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Doucoure, Boubacar & Agbossou, Kodjo & Cardenas, Alben, 2016. "Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data," Renewable Energy, Elsevier, vol. 92(C), pages 202-211.
    7. Prema, V. & Rao, K. Uma, 2015. "Development of statistical time series models for solar power prediction," Renewable Energy, Elsevier, vol. 83(C), pages 100-109.
    8. Nah-Oak Song & Ji-Hye Lee & Hak-Man Kim & Yong Hoon Im & Jae Yong Lee, 2015. "Optimal Energy Management of Multi-Microgrids with Sequentially Coordinated Operations," Energies, MDPI, vol. 8(8), pages 1-20, August.
    9. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    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. Zhang, Dongdong & Li, Chunjiao & Goh, Hui Hwang & Ahmad, Tanveer & Zhu, Hongyu & Liu, Hui & Wu, Thomas, 2022. "A comprehensive overview of modeling approaches and optimal control strategies for cyber-physical resilience in power systems," Renewable Energy, Elsevier, vol. 189(C), pages 1383-1406.
    2. Negri, Simone & Giani, Federico & Blasuttigh, Nicola & Massi Pavan, Alessandro & Mellit, Adel & Tironi, Enrico, 2022. "Combined model predictive control and ANN-based forecasters for jointly acting renewable self-consumers: An environmental and economical evaluation," Renewable Energy, Elsevier, vol. 198(C), pages 440-454.
    3. Yang, Lihua & Wu, Xiao, 2024. "Net-zero carbon configuration approach for direct air carbon capture based integrated energy system considering dynamic characteristics of CO2 adsorption and desorption," Applied Energy, Elsevier, vol. 358(C).

    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. Bogdanov, Dmitrii & Toktarova, Alla & Breyer, Christian, 2019. "Transition towards 100% renewable power and heat supply for energy intensive economies and severe continental climate conditions: Case for Kazakhstan," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    2. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.
    3. Zheng, Jianqin & Zhang, Haoran & Dai, Yuanhao & Wang, Bohong & Zheng, Taicheng & Liao, Qi & Liang, Yongtu & Zhang, Fengwei & Song, Xuan, 2020. "Time series prediction for output of multi-region solar power plants," Applied Energy, Elsevier, vol. 257(C).
    4. Olivia Francesca B. Agua & Robert Joseph A. Basilio & Mc Erschad D. Pabillan & Michael T. Castro & Philipp Blechinger & Joey D. Ocon, 2020. "Decentralized versus Clustered Microgrids: An Energy Systems Study for Reliable Off-Grid Electrification of Small Islands," Energies, MDPI, vol. 13(17), pages 1-22, August.
    5. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    6. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
    7. Zhang, Chao & Ma, Yunfeng & Mi, Zengqiang & Yang, Fan & Zhang, Long, 2024. "A rolling-horizon cleaning recommendation system for dust removal of industrial PV panels," Applied Energy, Elsevier, vol. 353(PB).
    8. Yang, Jie & Ma, Tieding & Ma, Kai & Yang, Bo & Guerrero, Josep M. & Liu, Zhixin, 2021. "Trading mechanism and pricing strategy of integrated energy systems based on credit rating and Bayesian game," Energy, Elsevier, vol. 232(C).
    9. Pascasio, Jethro Daniel A. & Esparcia, Eugene A. & Castro, Michael T. & Ocon, Joey D., 2021. "Comparative assessment of solar photovoltaic-wind hybrid energy systems: A case for Philippine off-grid islands," Renewable Energy, Elsevier, vol. 179(C), pages 1589-1607.
    10. Ana Lagos & Joaquín E. Caicedo & Gustavo Coria & Andrés Romero Quete & Maximiliano Martínez & Gastón Suvire & Jesús Riquelme, 2022. "State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems," Energies, MDPI, vol. 15(18), pages 1-40, September.
    11. Maurizio Filippo Acciarri & Silvia Checola & Paolo Galli & Giacomo Magatti & Silvana Stefani, 2021. "Water Resource Management and Sustainability: A Case Study in Faafu Atoll in the Republic of Maldives," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    12. Dominković, D.F. & Weinand, J.M. & Scheller, F. & D'Andrea, M. & McKenna, R., 2022. "Reviewing two decades of energy system analysis with bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    13. Wang, Ni & Liu, Ziyi & Heijnen, Petra & Warnier, Martijn, 2022. "A peer-to-peer market mechanism incorporating multi-energy coupling and cooperative behaviors," Applied Energy, Elsevier, vol. 311(C).
    14. Han, Dongho & Lee, Jay H., 2021. "Two-stage stochastic programming formulation for optimal design and operation of multi-microgrid system using data-based modeling of renewable energy sources," Applied Energy, Elsevier, vol. 291(C).
    15. Castro, Michael T. & Pascasio, Jethro Daniel A. & Delina, Laurence L. & Balite, Paul Heherson M. & Ocon, Joey D., 2022. "Techno-economic and financial analyses of hybrid renewable energy system microgrids in 634 Philippine off-grid islands: Policy implications on public subsidies and private investments," Energy, Elsevier, vol. 257(C).
    16. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    17. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    18. Muhammad Naveed Akhter & Saad Mekhilef & Hazlie Mokhlis & Ziyad M. Almohaimeed & Munir Azam Muhammad & Anis Salwa Mohd Khairuddin & Rizwan Akram & Muhammad Majid Hussain, 2022. "An Hour-Ahead PV Power Forecasting Method Based on an RNN-LSTM Model for Three Different PV Plants," Energies, MDPI, vol. 15(6), pages 1-21, March.
    19. Khosravi, A. & Machado, L. & Nunes, R.O., 2018. "Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil," Applied Energy, Elsevier, vol. 224(C), pages 550-566.
    20. Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.

    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:eee:renene:v:179:y:2021:i:c:p:1271-1290. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.