Quantify Benefits of Home Energy Management System Under Dynamic Electricity Pricing

Xiao, Jingjie and Liu, Andrew and Pekny, Joseph (2012): Quantify Benefits of Home Energy Management System Under Dynamic Electricity Pricing. Published in: Proceedings of 31st USAEE/IAEE NORTH AMERICAN CONFERENCE (10 November 2012)

Abstract

Retail electricity rates have been kept flat for the past century due to the lack of advanced metering technology and infrastructure. The flat-rate structure prevents consumers from responding to the fluctuation of actual costs of electricity generation, which varies hourly (or even minute-by-minute). The absence of demand response leads to an electricity system that is overly built with costly assets, solely to maintain system reliability. One of the core visions of the future electricity system, referred to as Smart Grid, is to use advanced metering infrastructure (AMI) and information technology to enable dynamic electricity rates. The main goal of this paper is to present an approximate dynamic programming (ADP) based modeling and algorithm framework that can make home energy management systems capable of optimally managing the appliance usage using the information of anticipated whole electricity prices. The other goal of the paper is to use the modeling framework to provide numerical evidence to the debate that if dynamic rate structure is superior than the current flat rate structure in terms of reducing peak demand and overall electricity costs.

• Item Type: MPRA Paper
• Language: English
• Keywords: Electricity Markets, Electricity Pricing, Demand side management, dynamic programming
• Subjects: C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q41 - Demand and Supply ; Prices; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q48 - Government Policy

References

J. Bushnell, B. F. Hobbs, and F. A. Wolak, “When it comes to demand response, is FERC its own worst enemy?” The Electricity Journal, vol. 22, no. 8, pp. 9 – 18, 2009.

W. W. Hogan, “Demand response pricing in organized wholesale markets,” May 2010. [Online]

S. Andersen, “Saving the Smart Grid,” Public Utilities Fortnightly, pp.33 – 39, 2011.

W. B. Powell, Approximate Dynamic Programming: Solving the Curses of Dimensionality. 2007.

B.-M. Hodge, A. Shukla, S. Huang, G. Reklaitis, V. Venkatasubramanian, and J. Pekny, “Multi-paradigm modeling of the effects of PHEV adoption on electric utility usage levels and emissions,” Industrial & Engineering Chemistry Research, vol. 50, no. 9, pp. 5191–5203, 2011.