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Learning based cost optimal energy management model for campus microgrid systems

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  • Kim, Jangkyum
  • Oh, Hyeontaek
  • Choi, Jun Kyun

Abstract

The introduction of microgrids has enabled an efficient energy management in the system with installation of renewable energy sources. As one of the representative models of microgrid, various studies on campus microgrids (CMGs) have been conducted. In operation of CMG, various energy consumption resources and renewable energy are considered to minimize overall cost or peak power in the system. However, most of conventional researches only deal with performance analysis in terms of simulation by collecting data from the different environments. In this case, there are lack of consideration in power regulation or electricity cost which make it difficult to apply the researched energy operation technology to the actual power system. To solve the problem, this paper build a test-bed in an actual CMG environment and collect dataset through the various IoT sensors. In addition, uncertainties that occur through the various power resources are analyzed and used to derive net energy consumption scenarios. In this way, we propose a new cost optimal energy management model with the detailed analysis of power generation and consumption using various auxiliary IoT devices. Based on the real-world datasets from the implemented CMG, we show that the proposed analytical models and energy management model are feasible for actual environments. With satisfying the constraints, we show that the daily electricity cost could be reduced up to 2.16% and peak power is reduced up to 3% compared to the case without considering the uncertainties in CMG.

Suggested Citation

  • Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(C).
  • Handle: RePEc:eee:appene:v:311:y:2022:i:c:s0306261922001015
    DOI: 10.1016/j.apenergy.2022.118630
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    References listed on IDEAS

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    1. Minkyung Kim & Sangdon Park & Joohyung Lee & Yongjae Joo & Jun Kyun Choi, 2017. "Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data," Energies, MDPI, vol. 10(10), pages 1-20, October.
    2. Despotovic, Milan & Nedic, Vladimir & Despotovic, Danijela & Cvetanovic, Slobodan, 2016. "Evaluation of empirical models for predicting monthly mean horizontal diffuse solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 246-260.
    3. 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.
    4. Monfared, Houman Jamshidi & Ghasemi, Ahmad & Loni, Abdolah & Marzband, Mousa, 2019. "A hybrid price-based demand response program for the residential micro-grid," Energy, Elsevier, vol. 185(C), pages 274-285.
    5. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Kendall, Alissa & Træholt, Chresten, 2018. "Optimization of a biomass-integrated renewable energy microgrid with demand side management under uncertainty," Applied Energy, Elsevier, vol. 230(C), pages 836-844.
    6. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
    7. Thomas, Dimitrios & Deblecker, Olivier & Ioakimidis, Christos S., 2018. "Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule," Applied Energy, Elsevier, vol. 210(C), pages 1188-1206.
    8. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    9. Afrasiabi, Mousa & Mohammadi, Mohammad & Rastegar, Mohammad & Kargarian, Amin, 2019. "Multi-agent microgrid energy management based on deep learning forecaster," Energy, Elsevier, vol. 186(C).
    10. Wang, Dongxiao & Qiu, Jing & Reedman, Luke & Meng, Ke & Lai, Loi Lei, 2018. "Two-stage energy management for networked microgrids with high renewable penetration," Applied Energy, Elsevier, vol. 226(C), pages 39-48.
    11. Byeong-Cheol Jeong & Dong-Hwan Shin & Jae-Beom Im & Jae-Young Park & Young-Jin Kim, 2019. "Implementation of Optimal Two-Stage Scheduling of Energy Storage System Based on Big-Data-Driven Forecasting—An Actual Case Study in a Campus Microgrid," Energies, MDPI, vol. 12(6), pages 1-20, March.
    12. 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.
    13. Jangkyum Kim & Yohwan Choi & Seunghyoung Ryu & Hongseok Kim, 2017. "Robust Operation of Energy Storage System with Uncertain Load Profiles," Energies, MDPI, vol. 10(4), pages 1-15, March.
    14. J W Taylor, 2003. "Short-term electricity demand forecasting using double seasonal exponential smoothing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(8), pages 799-805, August.
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    1. Max Olinto Moreira & Betania Mafra Kaizer & Takaaki Ohishi & Benedito Donizeti Bonatto & Antonio Carlos Zambroni de Souza & Pedro Paulo Balestrassi, 2022. "Multivariate Strategy Using Artificial Neural Networks for Seasonal Photovoltaic Generation Forecasting," Energies, MDPI, vol. 16(1), pages 1-30, December.
    2. Amad Ali & Hafiz Abdul Muqeet & Tahir Khan & Asif Hussain & Muhammad Waseem & Kamran Ali Khan Niazi, 2023. "IoT-Enabled Campus Prosumer Microgrid Energy Management, Architecture, Storage Technologies, and Simulation Tools: A Comprehensive Study," Energies, MDPI, vol. 16(4), pages 1-19, February.

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