Abstract
Home automation is evolving with the objective of upgrading the living convenience. The load control is, however, conceived as its subsidiary function for economic benefits. In this chapter, the problem of home load controlling (HLC) is widely investigated through deterministic and probabilistic analysis. The behavior of plug-in hybrid electric vehicles (PHEVs) consumer, i.e., departure time, traveling time, and energy consumption, are assumed to be stochastic variables. Incorporation of these inherent uncertainties offers a solution with robust optimality in real world applications. More benefits are accordingly achievable compared with deterministic solutions. The optimization problem is formulated based on the mixed-integer programming (MIP) fashion since present commercial high-performance solvers guarantee the optimality of solutions. Numerical studies are conducted in order to illustrate the effectiveness of the model which clarifies the practicality of the proposed approach. A variety of sensitivity analyses are performed to demonstrate the effectiveness of the method in different conditions.
Keywords
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Mohsenian-Rad AH, Leon-Garcia A (2010) Optimal residential load control with price prediction in real-time electricity pricing environment. IEEE Trans Smart Grid 1:120–133
Du P, Lu N (2011) Appliance commitment for household load scheduling. IEEE Trans Smart Grid 2:411–419
Pedrasa MAA, Spooner TD, MacGill IF (2010) Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Trans Smart Grid 1:134–142
Darabi Z, Ferdowsi M (2011) Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile. IEEE Trans Sustain Energy 2:501–508
Clement-Nyns K, Haesen E, Driesen J (2010) The impact of charging plug-in hybrid electric vehicles on a residential distribution grid. IEEE Trans Power Syst 25:371–380
Vazquez S, Lukic SM, Galvan E, Franquelo LG, Carrasco JM (2010) Energy storage systems for transport and grid applications. IEEE Trans Indus Electron 57:3881–3895
Erol-Kantarci M, Mouftah HT (2011) Wireless sensor networks for cost-efficient residential energy management in the smart grid. IEEE Trans Smart Grid 2:314–325
Erol-Kantarci M, Mouftah HT (2010) Using wireless sensor networks for energy-aware homes in smart grids. IEEE Symposium on computers and communications (ISCC)
Conejo AJ, Morales JM, Baringo L (2010) Real-time demand response model. IEEE Trans Smart Grid 1:120–133
Rastegar M, Fotuhi-Firuzabad M, Aminifar F (2012) Load commitment in a smart home. J Appl Energy 96:45–54
Ipakchi A, Albuyeh F (2009) Grid of the future. IEEE Power Energy Mag 7(4):52–62
National household travel survey (2014) http://nhts.ornl.gov
Billinton R, Allen R (1996) Reliability evaluation of power system. Plenum Press
Baltimore gas and electric three-level summer’s tariffs (2012) http://www.bge.com/portal/site/bge/menuitem.dc72e1697738765822b75475da6176a0/
The infinite power of Texas, estimating PV system size and cost. SECO fact sheet, vol 24, pp 1–4, 2008
Brooke A, Kendrick D, Meeraus A, Raman R (2003) GAMS: a user’s guide. GAMS Development Corp., Washington, DC
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this chapter
Cite this chapter
Fotuhi-Firuzabad, M., Rastegar, M., Safdarian, A., Aminifar, F. (2014). Probabilistic Home Load Controlling Considering Plug-in Hybrid Electric Vehicle Uncertainties. In: Karki, R., Billinton, R., Verma, A. (eds) Reliability Modeling and Analysis of Smart Power Systems. Reliable and Sustainable Electric Power and Energy Systems Management. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1798-5_8
Download citation
DOI: https://doi.org/10.1007/978-81-322-1798-5_8
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1797-8
Online ISBN: 978-81-322-1798-5
eBook Packages: EnergyEnergy (R0)