Scenario-Based Stochastic Programming Strategy for Microgrid Energy Scheduling Considering Uncertainties

Abstract:

The inherent random and intermittence of the renewable energy resources pose a huge challenge to the Microgrid (MG) energy management systems (EMS). In order to mitigate the effects of uncertainties, we propose a novel two-stage stochastic programming model for the energy scheduling optimization by considering the uncertainties in solar and wind generation, and the plug-in electric vehicles (EV). The random nature of uncertainty is characterized by a scenarios generation approach based on autoregressive moving average (ARMA) model according to probability density function of each random variable. By use of the strategy of scenarios simulation, the stochastic problem is decomposed into the deterministic equivalent problem. The firefly algorithm (FA) is used to solve the equivalent model. The effectiveness and robust of proposed stochastic energy scheduling optimization strategy for MG is valid by comparison with the simulation results of deterministic method.

• Periodical: Applied Mechanics and Materials (Volumes 672-674)
• Pages: 1322-1328
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.672-674.1322
• Online since: October 2014
• Authors: He Peng Li*, Chuan Zhi Zang, Peng Zeng, Hai Bin Yu, Zhong Wen Li
• Keywords: Energy Scheduling Optimization, Microgrids (MGs), Scenarios Generation, Stochastic Programming

References

[1] R. H. Lasseter, MicroGrids, in Power Engineering Society Winter Meeting, 2002. IEEE, 2002, pp.305-308 vol. 1.

[2] M. Strelec, K. Macek, and A. Abate, Modeling and Simulation of a Microgrid as a Stochastic Hybrid System, 2012 3rd Ieee Pes Innovative Smart Grid Technologies Europe (Isgt Europe), (2012).

DOI: 10.1109/isgteurope.2012.6465655

[3] S. Chakraborty, M. D. Weiss, and M. G. Simoes, Distributed Intelligent Energy Management System for a Single-Phase High-Frequency AC Microgrid, Industrial Electronics, IEEE Transactions on, vol. 54, pp.97-109, (2007).

DOI: 10.1109/tie.2006.888766

[4] C. Chen, s. duan, T. Cai, B. Liu, and G. Hu, Smart energy management system for optimal microgrid economic operation, Renewable Power Generation, IET, vol. 5, pp.258-267, (2011).

DOI: 10.1049/iet-rpg.2010.0052

[5] P. O. Kriett and M. Salani, Optimal control of a residential microgrid, Energy, vol. 42, pp.321-330, Jun (2012).

DOI: 10.1016/j.energy.2012.03.049

[6] A. Seon-Ju, N. Soon-Ryul, C. Joon-Ho, and M. Seung-Il, Power Scheduling of Distributed Generators for Economic and Stable Operation of a Microgrid, Smart Grid, IEEE Transactions on, vol. 4, pp.398-405, (2013).

DOI: 10.1109/tsg.2012.2233773

[7] S. Mohammadi, S. Soleymani, and B. Mozafari, Scenario-based stochastic operation management of MicroGrid including Wind, Photovoltaic, Micro-Turbine, Fuel Cell and Energy Storage Devices, International Journal of Electrical Power & Energy Systems, vol. 54, pp.525-535, Jan (2014).

DOI: 10.1016/j.ijepes.2013.08.004

[8] T. Niknam, R. Azizipanah-Abarghooee, and M. R. Narimani, An efficient scenario-based stochastic programming framework for multi-objective optimal micro-grid operation, Applied Energy, vol. 99, pp.455-470, Nov (2012).

DOI: 10.1016/j.apenergy.2012.04.017

[9] W. Su, J. Wang, and J. Roh, Stochastic Energy Scheduling in Microgrids With Intermittent Renewable Energy Resources, Smart Grid, IEEE Transactions on, vol. PP, pp.1-9, (2013).

DOI: 10.1109/tsg.2013.2280645

[10] National Household Travel Survey Pre- and Post-9/11 Data Documentation. Available: National Household Travel SurveyPre- and Post-9/11 Data Documentation | Bureau of Transportation Statistics.

[11] W. Ting, Y. Qiang, B. Zhejing, and Y. Wenjun, Coordinated Energy Dispatching in Microgrid With Wind Power Generation and Plug-in Electric Vehicles, Smart Grid, IEEE Transactions on, vol. 4, pp.1453-1463, (2013).

DOI: 10.1109/tsg.2013.2268870

[12] J. Dupacova, N. Growe-Kuska, and W. Romisch, Scenario reduction in stochastic programming - An approach using probability metrics, Mathematical Programming, vol. 95, pp.493-511, Mar (2003).

DOI: 10.1007/s10107-002-0331-0

[13] E. Valsera-Naranjo, A. Sumper, R. Villafafila-Robles, and D. Martinez-Vicente, Probabilistic Method to Assess the Impact of Charging of Electric Vehicles on Distribution Grids, Energies, vol. 5, pp.1503-1531, May (2012).

DOI: 10.3390/en5051503