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Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation

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  • Bakhtiari, Hamed
  • Zhong, Jin
  • Alvarez, Manuel

Abstract

Due to the lack of available flexibility sources to cope with different uncertainties in the real-time operation of stand-alone renewable energy-based microgrids, the stochastic behavior of uncertainty sources needs to be included in the planning stage. Since there is a high association between some of the uncertainty sources, defining a proper time series to represent the behavior of each source of uncertainty is a challenging issue. Consequently, uncertainty sources should be modeled in such a way that the designed microgrid be able to cope with all scenarios from probability and impact viewpoints. This paper proposes a modified Metropolis–coupled Markov chain Monte Carlo (MC)3 simulation to predict the stochastic behavior of different uncertainty sources in the planning of a stand-alone renewable energy-based microgrid. Solar radiation, wind speed, the water flow of a river, load consumption, and electricity price have been considered as primary sources of uncertainty. A novel data classification method is introduced within the (MC)3 simulation to model the time-dependency and the association between different uncertainty sources. Moreover, a novel curve-fitting approach is proposed to improve the accuracy of representing the multimodal distribution functions, modeling the Markov chain states, and the long-term probability of uncertainty sources. The predicted representative time series with the proposed modified (MC)3 model is benchmarked against the retrospective model, the long-term historical data, and the simple Monte Carlo simulation model to capture the stochastic behavior of uncertainty sources. The results show that the proposed model represents the probability distribution function of each source of uncertainty, the continuity of samples, time dependency, the association between different uncertainty sources, short-term and long-term trends, and the seasonality of uncertainty sources. Finally, results confirm that the proposed modified (MC)3 can appropriately predict all scenarios with high probability and impact.

Suggested Citation

  • Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    • Handle: RePEc:eee:appene:v:290:y:2021:i:c:s0306261921002373
    DOI: 10.1016/j.apenergy.2021.116719
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    References listed on IDEAS

    as
    1. Narayan, Apurva & Ponnambalam, Kumaraswamy, 2017. "Risk-averse stochastic programming approach for microgrid planning under uncertainty," Renewable Energy, Elsevier, vol. 101(C), pages 399-408.
    2. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    3. Hilbers, Adriaan P. & Brayshaw, David J. & Gandy, Axel, 2019. "Importance subsampling: improving power system planning under climate-based uncertainty," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    5. Ehsan, Ali & Yang, Qiang, 2019. "Scenario-based investment planning of isolated multi-energy microgrids considering electricity, heating and cooling demand," Applied Energy, Elsevier, vol. 235(C), pages 1277-1288.
    6. Lotfi, Hossein & Khodaei, Amin, 2017. "Hybrid AC/DC microgrid planning," Energy, Elsevier, vol. 118(C), pages 37-46.
    7. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    8. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2017. "Generation expansion planning with high share of renewables of variable output," Applied Energy, Elsevier, vol. 190(C), pages 1275-1288.
    9. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    10. Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    11. Hemmati, Reza & Saboori, Hedayat & Siano, Pierluigi, 2017. "Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems," Energy, Elsevier, vol. 134(C), pages 699-708.
    12. Sanajaoba Singh, Sarangthem & Fernandez, Eugene, 2018. "Modeling, size optimization and sensitivity analysis of a remote hybrid renewable energy system," Energy, Elsevier, vol. 143(C), pages 719-731.
    13. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    14. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    15. al Irsyad, Muhammad Indra & Halog, Anthony & Nepal, Rabindra, 2019. "Renewable energy projections for climate change mitigation: An analysis of uncertainty and errors," Renewable Energy, Elsevier, vol. 130(C), pages 536-546.
    16. Poncelet, Kris & Delarue, Erik & Six, Daan & Duerinck, Jan & D’haeseleer, William, 2016. "Impact of the level of temporal and operational detail in energy-system planning models," Applied Energy, Elsevier, vol. 162(C), pages 631-643.
    17. Bilgili, Mehmet & Ozbek, Arif & Sahin, Besir & Kahraman, Ali, 2015. "An overview of renewable electric power capacity and progress in new technologies in the world," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 323-334.
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    7. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.

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