Hamadi BOUAICHA, Emily CRAPARO, Habib DALLAGI, Samir NEJIM

Dynamic optimal management of a hybrid microgrid based on weather forecasts

Hybrid microgrids containing both renewable and conventional power sources are becoming increasingly attractive for a variety of reasons. However, intermittency of renewable power production and uncertainty in future load prediction increase risks of electric grid instability and, by consequence, restrict the portion of renewable power production in microgrids. In order, to prefigure the upcoming renewable power production, particularly, wind power and photovoltaic power, we suggest using weather forecasts. In addition to illustrating short term renewable power prediction based on ensemble weather forecasts, this paper focuses on optimizing the management of distributed power generation, power storage, and power exchange with the commercial electric grid. Establishing an optimal operating plan for the grid makes the integration of the predicted renewable production more efficient. The optimization problem is formulated as an integer linear program. The operating plan is initially optimized over the upcoming day and then regularly updated and incremented to cover a longer time horizon. After analyzing the obtained plans, we test and evaluate them relative to the observed weather conditions. Finally, we investigate some practical problems that had arisen and we apply a time cascade optimization technique to mitigate the effects of initial conditions and end-of-horizon effects, as well as to take advantage of updated forecasts.

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1] Gandoman F, Ahmadi A, Sharaf A, Siano P, Pou J et al. Review of FACTS technologies and applications for power quality in smart grids with renewable energy systems. Renewable and Sustainable Energy Reviews 2018; 82: 502-514.
[2] Du S, Dekka A, Wu B, Zargari N. Modular Multilevel Converters: Analysis, Control, and Applications. USA: John Wiley & Sons, 2017.
[3] Shahnazian F, Adabi J, Pouresmaeil E, Catalão J. Interfacing modular multilevel converters for grid integration of renewable energy sources. Electric Power Systems Research 2018; 160: 439-449. doi: 10.1016/j.epsr.2018.03.014
[4] Liang X. Emerging power quality challenges due to integration of renewable energy sources. IEEE Transactions on Industry Applications 2017; 53(2): 855-866. doi: 10.1109/TIA.2016.2626253
[5] Vera G, Yimy E, Dufo-López R, Bernal-Agustín JL. Energy management in microgrids with renewable energy sources: A literature review. Applied Sciences 2019; 9 (18): 3854. doi: 10.3390/app9183854
[6] Vakkapatla KB, Pinni SV. Generation rescheduling using multiobjective bilevel optimization. Turkish Journal of Electrical Engineering & Computer Sciences 2019; 27(3): 2086-2096. doi: 10.3906/elk-1804-77
[7] Zhang J, Wu Y, Guo Y et al. A hybrid harmony search algorithm with differential evolution for day-ahead scheduling problem of a microgrid with consideration of power flow constraints. Applied Energy 2016; 183 (C): 791-804. doi: 10.1016/j.apenergy.2016.09.035
[8] Conteh F, Tobaru S, Howlader H et al. Energy management systems for hybrid distributed generation sources in grid connected and stand-alone micro-grids. Journal of Renewable and Sustainable Energy 2017; 9 (6): 065301. doi: 10.1063/1.4998773
[9] Raza MQ, Nadarajah M, Ekanayake C. On recent advances in PV output power forecast. Solar Energy 2016; 136: 125-144. doi:10.1016/j.solener.2016.06.073
[10] Bouaicha H, Craparo E, Dallagi H, Nejim S. Economic scheduling of a hybrid microgrid based on weather forecasts. In: 2017 International Conference on Advanced Systems& Electric Technologies; Hammamet, Tunisia; 2017. pp. 110-117.
[11] Obi M, Jensen SM, Ferris JB, Bass RB. Calculation of levelized costs of electricity for various electrical energy storage systems. Renewable and Sustainable Energy Reviews 2017; 67: 908-920. doi: 10.1016/j.rser.2016.09.043
[12] Letcher TM. Wind Energy Engineering: A Handbook for Onshore and Offshore Wind Turbines. London Wall, London, United Kingdom: Academic Press, 2017.
[13] Villa J, Martí A. Impact of the spectrum in the annual energy production of multijunction solar cells. IEEE Journal of Photovoltaics 2017; 7 (5): 1479-1484. doi: 10.1109/JPHOTOV.2017.2717038
[14] Chen L, Liu Y. Scheduling strategy of hybrid wind-photovoltaic-hydro power generation system. In: 2012 Interna- tional Conference on Sustainable Power Generation and Supply; Hangzhou, China; 2012. pp.1-20.
[15] Jiang X, Chen H, Liu X, Xiang T. Application of the forecast error on unit commitment with renewable power integration. In: IEEE 2015 Power & Energy Society General Meeting; Denver, CO, USA; 2015. pp.1-5.
[16] Wu W, Wang D, Arapostathis A, Davey K. Optimal power generation scheduling of a shipboard power system. In: IEEE 2007 Electric Ship Technologies Symposium; Arlington, VA, USA; 2007. pp. 519-522.
[17] Mathews JH, Fink KD. Numerical Methods Using MATLAB. Upper Saddle River, NJ, USA: Pearson, 2004.
[18] Zhu J. Optimization of Power System Operation. John Wiley & Sons, 2015. [19] Sönmez, Y. Estimation of fuel cost curve parameters for thermal power plants using the ABC algorithm. Turkish Journal of Electrical Engineering & Computer Sciences 2013; 21 (1): 1827-1841. doi:10.3906/elk-1203-10
[20] Zavala VM, Constantinescu EM, Anitescu M. Economic impacts of advanced weather forecasting on energy system operations. In: IEEE 2010 Innovative Smart Grid Technologies Conference; Gothenburg, Sweden; 2010. pp.1-7.
[21] Wang Y, Chen Q, Sun M, Kang C, Xia Q. An ensemble forecasting method for the aggregated load with subprofiles. IEEE Transactions on Smart Grid 2018; 9 (4): 3906-3908.
[22] Esener I, Yuksel T, Kurban M. Short-term load forecasting without meteorological data using AI-based structures. Turkish Journal of Electrical Engineering & Computer Sciences 2015; 23 (2): 370-380. doi:10.3906/elk-1209-28
[23] Ma J, Yang M, Han X, Li Z. Ultra-short-term wind generation forecast based on multivariate empirical dynamic modeling. IEEE Transactions on Industrial Applications 2018; 54 (2): 1029-1038. doi:10.1109/TIA.2017.2782207