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ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs

Author

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  • Srete Nikolovski

    (Power Engineering Department, Faculty of Electrical Engineering, Computer Science and Information Technology, University of Osijek, Osijek 31000, Croatia)

  • Hamid Reza Baghaee

    (Department of Electrical Engineering, Amirkabir University of Technology, 15875-4413 Tehran, Iran)

  • Dragan Mlakić

    (Department of Measurement and Network Management in Electrical Energy Systems, Distribution Area, Centar“, JP Elektroprivreda HZ HB“ d.d, Mostar, 88 000 Mostar, Bosnia and Herzegovina)

Abstract

One of the most crucial and economically-beneficial tasks for energy customers is peak load curtailment. On account of the fast response of renewable energy resources (RERs) such as photovoltaic (PV) units and battery energy storage system (BESS), this task is closer to be efficiently implemented. Depends on the customer peak load demand and energy characteristics, the feasibility of this strategy may vary. When adaptive neuro-fuzzy inference system (ANFIS) is exploited for forecasting, it can provide many benefits to address the above-mentioned issues and facilitate its easy implementation, with short calculating time and re-trainability. This paper introduces a data-driven forecasting method based on fuzzy logic (FL) for optimized peak load reduction. First, the amount of energy generated by PV is forecasted using ANFIS which conducts output trend, and then, the BESS capacity is calculated according to the forecasted results. The trend of the load power is then decomposed in Cartesian plane into two parts, namely left and right from load peak, for the sake of searching for equal BESS capacity. Network switching sequence over consumption is provided by a fuzzy logic controller (FLC) considering BESS capacity and PV energy output. Finally, to prove the effectiveness of the proposed ANFIS-based peak power shaving/curtailment method, offline digital time-domain simulations have been performed on a test microgrid system using the data gathered from a real-life practical test microgrid system in the MATLAB/Simulink environment and the results have been experimentally verified by testing on a practical microgrid system with real-life data obtained from smart meters and also, compared with several previously-reported methods.

Suggested Citation

  • Srete Nikolovski & Hamid Reza Baghaee & Dragan Mlakić, 2018. "ANFIS-Based Peak Power Shaving/Curtailment in Microgrids Including PV Units and BESSs," Energies, MDPI, vol. 11(11), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2953-:d:179020
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    References listed on IDEAS

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    1. Ponta, Linda & Raberto, Marco & Teglio, Andrea & Cincotti, Silvano, 2018. "An Agent-based Stock-flow Consistent Model of the Sustainable Transition in the Energy Sector," Ecological Economics, Elsevier, vol. 145(C), pages 274-300.
    2. Yunpeng Guo & Liyan Zhang & Junhua Zhao & Fushuan Wen & Abdus Salam & Jianwei Mao & Liang Li, 2017. "Networked Control of Electric Vehicles for Power System Frequency Regulation with Random Communication Time Delay," Energies, MDPI, vol. 10(5), pages 1-23, May.
    3. Baghaee, H.R. & Mirsalim, M. & Gharehpetian, G.B. & Talebi, H.A., 2016. "Reliability/cost-based multi-objective Pareto optimal design of stand-alone wind/PV/FC generation microgrid system," Energy, Elsevier, vol. 115(P1), pages 1022-1041.
    4. Xianyong Zhang & Yaohong Huang & Li Li & Wei-Chang Yeh, 2018. "Power and Capacity Consensus Tracking of Distributed Battery Storage Systems in Modular Microgrids," Energies, MDPI, vol. 11(6), pages 1-25, June.
    5. Chao Lu & Hanchen Xu & Xin Pan & Jie Song, 2014. "Optimal Sizing and Control of Battery Energy Storage System for Peak Load Shaving," Energies, MDPI, vol. 7(12), pages 1-15, December.
    6. Allegra De Filippo & Michele Lombardi & Michela Milano, 2017. "User-Aware Electricity Price Optimization for the Competitive Market," Energies, MDPI, vol. 10(9), pages 1-23, September.
    7. Min-Kyu Baek & Duehee Lee, 2017. "Spatial and Temporal Day-Ahead Total Daily Solar Irradiation Forecasting: Ensemble Forecasting Based on the Empirical Biasing," Energies, MDPI, vol. 11(1), pages 1-18, December.
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    Cited by:

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    2. Jin Zhu & Dequn Zhou & Zhengning Pu & Huaping Sun, 2019. "A Study of Regional Power Generation Efficiency in China: Based on a Non-Radial Directional Distance Function Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
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    4. Duberney Murillo-Yarce & José Alarcón-Alarcón & Marco Rivera & Carlos Restrepo & Javier Muñoz & Carlos Baier & Patrick Wheeler, 2020. "A Review of Control Techniques in Photovoltaic Systems," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    5. Hsen Abidi & Lilia Sidhom & Ines Chihi, 2023. "Systematic Literature Review and Benchmarking for Photovoltaic MPPT Techniques," Energies, MDPI, vol. 16(8), pages 1-45, April.
    6. Hyung Tae Kim & Young Gyu Jin & Yong Tae Yoon, 2019. "An Economic Analysis of Load Leveling with Battery Energy Storage Systems (BESS) in an Electricity Market Environment: The Korean Case," Energies, MDPI, vol. 12(9), pages 1-16, April.
    7. Wenhao Zhuo & Andrey V. Savkin, 2019. "Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting," Energies, MDPI, vol. 12(15), pages 1-17, July.
    8. Concettina Marino & Antonino Nucara & Maria Francesca Panzera & Matilde Pietrafesa & Alfredo Pudano, 2020. "Economic Comparison Between a Stand-Alone and a Grid Connected PV System vs. Grid Distance," Energies, MDPI, vol. 13(15), pages 1-22, July.
    9. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).
    10. Haoming Liu & Muhammad Yasir Ali Khan & Xiaoling Yuan, 2023. "Hybrid Maximum Power Extraction Methods for Photovoltaic Systems: A Comprehensive Review," Energies, MDPI, vol. 16(15), pages 1-64, July.

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