Open Access
E3S Web Conf.
Volume 238, 2021
100RES 2020 – Applied Energy Symposium (ICAE), 100% RENEWABLE: Strategies, Technologies and Challenges for a Fossil Free Future
Article Number 02005
Number of page(s) 6
Section Hybrid Systems
Published online 16 February 2021
  1. M. Franz, N. Peterschmidt, M. Rohrer, and B. Kondev, “Mini-grid Policy Toolkit, ” 2014. [Google Scholar]
  2. REN21, Renewables 2018 Global Status Report. 2018. [Google Scholar]
  3. C. D. Barley and C. B. Winn, “Optimal dispatch strategy in remote hybrid power systems, ” Sol. Energy, Vol. 58, pp. 165–179, Oct. 1996. [Google Scholar]
  4. L. Moretti, S. Polimeni, L. Meraldi, P. Raboni, S. Leva, and G. Manzolini, “Assessing the impact of a two-layer predictive dispatch algorithm on design and operation of off-grid hybrid microgrids, ” Renew. Energy, Vol. 143, pp. 1439–1453, 2019. [Google Scholar]
  5. D. Fioriti, D. Poli, and G. Lutzemberger, “Rolling-horizon scheduling strategies for off-grid systems: on the optimal redispatching frequency and the effects of forecasting errors, ” in 19th IEEE International Conference on Environment and Electrical Engineering (EEEIC), 2019. [Google Scholar]
  6. D. S. Pandžić, H. Dvorkin, Y. Qiu, T. Wang, and Y. Kirschen, “Toward Cost-Efficient and Reliable Unit Commitment Under Uncertainty, ” IEEE Trans. Power Syst., no. 99, pp. 1–13, 2016. [Google Scholar]
  7. Q. P. Zheng, J. Wang, and A. L. Liu, “Stochastic Optimization for Unit Commitment — A Review, ” IEEE Trans. Power Syst., no. 99, pp. 1–12, 2015. [Google Scholar]
  8. S. Y. Abujarad, M. W. Mustafa, and J. J. Jamian, “Recent approaches of unit commitment in the presence of intermittent renewable energy resources: A review, ” Renew. Sustain. Energy Rev., Vol. 70, pp. 215–223, 2017. [Google Scholar]
  9. A. J. Kleywegt, A. Shapiro, and T. Homem-deMello, “The Sample Average Approximation Method for Stochastic Discrete Optimization, ” SIAM J. Optim., Vol. 12, no. 2, pp. 479–502, 2002. [Google Scholar]
  10. H. Quan, D. Srinivasan, and A. Khosravi, “Integration of renewable generation uncertainties into stochastic unit commitment considering reserve and risk: A comparative study, ” Energy, Vol. 103, pp. 735–745, 2016. [Google Scholar]
  11. D. E. Olivares, J. D. Lara, C. A. Canizares, and M. Kazerani, “Stochastic-Predictive Energy Management System for Isolated Microgrids, ” IEEE Trans. Smart Grid, Vol. 6, no. 6, pp. 1–9, 2015. [Google Scholar]
  12. G. Morales-España, Á. Lorca, and M. M. de Weerdt, “Robust unit commitment with dispatchable wind power, ” Electr. Power Syst. Res., Vol. 155, pp. 58–66, 2018. [Google Scholar]
  13. M. Håberg, “Fundamentals and recent developments in stochastic unit commitment, ” Int. J. Electr. Power Energy Syst., Vol. 109, pp. 38–48, Jul. 2019. [Google Scholar]
  14. Y. Wang, Y. Liu, and D. S. Kirschen, “Scenario Reduction With Submodular Optimization, ” IEEE Trans. Power Syst., Vol. 32, no. 3, pp. 2479–2480, 2017. [Google Scholar]
  15. D. Fioriti and D. Poli, “A novel stochastic method to dispatch microgrids using Monte Carlo scenarios, ” Electr. Power Syst. Res., Vol. 175, no. October 2019, 2019. [Google Scholar]
  16. E. Arriagada, E. López, M. López, R. BlascoGimenez, C. Roa, and M. Poloujadoff, “A probabilistic economic dispatch model and methodology considering renewable energy, demand and generator uncertainties, ” Electr Pow Syst Res, Vol. 121, pp. 325–332, 2015. [Google Scholar]
  17. R. Jabbari-Sabet, S.-M. Moghaddas-Tafreshi, and S.-S. Mirhoseini, “Microgrid operation and management using probabilistic reconfiguration and unit commitment, ” Int. J. Electr. Power Energy Syst., Vol. 75, pp. 328–336, 2016. [Google Scholar]
  18. N. Nikmehr, S. Najafi-Ravadanegh, and A. Khodaei, “Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty, ” Appl. Energy, Vol. 198, pp. 267–279, 2017. [Google Scholar]
  19. R. Siddaiah and R. P. Saini, “A review on planning, configurations, modeling and optimization techniques of hybrid renewable energy systems for off grid applications, ” Renew. Sustain. Energy Rev., Vol. 58, pp. 376–396, 2016. [Google Scholar]
  20. L. Moretti, M. Astolfi, C. Vergara, E. Macchi, J. I. Pérez-Arriaga, and G. Manzolini, “A design and dispatch optimization algorithm based on mixed integer linear programming for rural electrification, ” Appl. Energy, Vol. 233–234, no. November 2018, pp. 1104–1121, 2019. [Google Scholar]
  21. M. Giuntoli, P. Pelacchi, and D. Poli, “On the use of simplified reactive power flow equations for purposes of fast reliability assessment, ” IEEE EuroCon 2013, no. July, pp. 992–997, 2013. [Google Scholar]

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