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Data Forecasting and Storage Sizing for PV Battery System Using Fuzzy Markov Chain Model

  • Research Article-Electrical Engineering
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Abstract

Although photovoltaic (PV) power is a green energy source, the high output variability of PV power generation leads to lags in network availability. To increase PV power plant reliability, an energy storage system can be incorporated. However, improper selection of storage size increases system cost or decreases network availability due to over- or under-sizing of the storage capacity, respectively. For this reason, we develop a generalized Markov chain-based battery charging–discharging procedure for determining proper storage size. In this work, multi-objective clustering and fuzzy decision-making (FDM) techniques for selecting the most optimal storage size with optimal availability are also proposed to avoid issues associated with conventional selection procedures. The reliability of our approach is analyzed based on forecasted data. As better prediction yields more dispatchable storage sizing, thereby improving system reliability, a first-order fuzzy time-series, Markov chain-based prediction method is used owing to its high prediction efficiency. To explore PV power generation performance in both cool and hot, sunny conditions, the temperature effect is considered in the storage sizing–availability analysis.

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References

  1. Robinson, D.; Arent, D.; Johnson, L.: Impact of distributed energy resources on the reliability of critical telecommunications facilities. In: INTELEC 06-Twenty-Eighth International Telecommunications Energy Conference, pp 1–7, (2006)

  2. Wang, M., Zhong, J.: Islanding of systems of distributed generation using optimization methodology. In: IEEE Power and Energy Society General Meeting, pp. 1–7, (2012)

  3. Esmat, A.; Magdy, A. El Khattam, W.; El Bakly, A.M.: A novel energy management system using ant colony optimization for micro-grids. In: 2013 3rd International Conference on Electric Power and Energy Conversion Systems, pp. 1–6, (2013)

  4. Yassami, H.; Moeini, A.; Rafiei, S.M.R.; Darabi, A.; Bagheri, A.: Optimal distributed generation planning considering reliability, cost of energy and power loss. Sci. Res. Essays 6(9), 1963–1976 (2011)

    Article  Google Scholar 

  5. Phonrattanasak, P.: Optimal placement of wind farm on the power system using multi-objective bees algorithm. In: Proceedings of the World Congress on Engineering 2(3), pp 4–8, (2011)

  6. Sayed, R.; Hegazy, Y.G.; Mostafa, M.A.: Modeling of photovoltaic-based power stations for reliability studies using Markov chains. In: 2013 International Conference on Renewable Energy Research and Applications (ICRERA), pp. 667–673, (2013)

  7. Shayeghi, H.; Moradzadeh, M.; Hashemi, Y.; Saif, M., Vandevelde, L.: Wind-PV-storage optimal environomic design using multi-objective artificial bee colony. In: 2015 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), pp. 1–5, (2015)

  8. Wang, H.; Huang, J.: Joint investment and operation of microgrid. IEEE Trans Smart Grid 8(2), 833–845 (2017)

    Google Scholar 

  9. Elsaiah, S.; Benidris, M.; Mitra, J.: Reliability improvement of power distribution system through feeder reconfiguration. In: 2014 International Conference on Probabilistic Methods Applied to Power Systems pp. 1–6, (2014)

  10. Venu, C.; Riffonneau, Y.; Bacha, S.; Baghzouz, Y.: Battery storage system sizing in distribution feeders with distributed photovoltaic systems. In: IEEE Bucharest PowerTech, pp. 1–5, (2009)

  11. Bhuiyan, M.M.H.; Asgar, M.A.: Sizing of a stand-alone photovoltaic power system at Dhaka. Renew. Energy 28(6), 929–938 (2003)

    Article  Google Scholar 

  12. Photovoltaic (PV)-electrical calculations. http://myelectrical.com/notes/entryid/225/photovoltaic-pv-electrical-calculations. Accessed 18 April 2016

  13. Arjyadhara, P.; Chitralekha, J.: Analysis of solar PV cell performance with changing irradiance and temperature. Int. J. Eng. Comput. Sci. 2(1), 214–220 (2013)

    Google Scholar 

  14. Song, J.; Member, S.; Krishnamurthy, V.: Development of a Markov chain–based energy storage model for power supply availability assessment of photovoltaic generation plants. Energy 4(2), 491–500 (2013)

    Google Scholar 

  15. Chen, S.; Hsu, C.: A new method to forecast enrollments using fuzzy time series. Int. J. Appl. Sci. Eng. 2, 234–244 (2004)

    Google Scholar 

  16. Zarandi, M.H.F.; Molladavoudi, A.; Ali Beigi, M.H.: A new method for temperature prediction and the TAIFEX forecasting based on fuzzy logical relationship and double interval division. In 2009 IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1543–1547 (2009)

  17. Lee, L.W.; Wang, L.H.; Chen, S.M.: Temperature prediction and TAIFEX forecasting based on high-order fuzzy logical relationships and genetic simulated annealing techniques. Expert Syst. Appl. 34(1), 328–336 (2008)

    Article  Google Scholar 

  18. Vamitha, V.; Jeyanthi, M.; Rajaram, S.; Revathi, T.: Temperature prediction using fuzzy time series and multivariate Markov chain. IJFMS 2(3), 217–230 (2012)

    Google Scholar 

  19. Liu, J.; Li, S.; Jia, S.: A prediction model based on neural network and fuzzy Markov chain. In: 2008 7th World Congress on Intelligent Control and Automation. pp 790–793, (2008)

  20. Li, S.-T.; Cheng, Y.-C.: A stochastic HMM-based forecasting model for fuzzy time series. IEEE Trans. Syst. Man Cybern. B Cybern. 40(5), 1255–1266 (2010)

    Article  Google Scholar 

  21. Tsaur, R.-C.: A fuzzy time series-Markov chain model with an application to forecast the exchange rate between the Taiwan and US dollar. IJICIC 8(7), 4931–4942 (2012)

    Google Scholar 

  22. Morse, J.N.: Reducing the size of the nondominated set: pruning by clustering. Comput. Oper. Res. 7(1–2), 55–66 (1980)

    Article  Google Scholar 

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Acknowledgment

The authors acknowledge the support of King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, through Grant IN161043.

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  1. King Fahd University of Petroleum and Minerals, Daharan, Eastern Province, Saudi Arabia

    M Ilius Pathan & Mohammad Al-Muhaini

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  1. M Ilius Pathan
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  2. Mohammad Al-Muhaini
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Correspondence to M Ilius Pathan.

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Pathan, M.I., Al-Muhaini, M. Data Forecasting and Storage Sizing for PV Battery System Using Fuzzy Markov Chain Model. Arab J Sci Eng 45, 6675–6686 (2020). https://doi.org/10.1007/s13369-020-04623-2

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  • DOI: https://doi.org/10.1007/s13369-020-04623-2

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