Sizing the Electrical Grid

Transformers and storage batteries in the electrical grid must be provisioned or sized just as routers and buffers must be sized in the Internet. We prove the formal equivalence between these two systems and use this insight to apply teletraffic theory to sizing the electrical grid, obtaining the capacity region corresponding to a given transformer and storage size. To validate our analysis, we conduct a fine-grained measurement study of household electrical load. We compare numerical simulations using traces from this study with results from teletraffic theory. We show not only that teletraffic theory agrees well with numerical simulations but also that it closely matches with the heuristics used in current practice. Moreover, our analysis permits us to develop sizing rules for battery storage electrical grid, advancing the state of the art.

[1]  D. Mitra,et al.  Stochastic theory of a data-handling system with multiple sources , 1982, The Bell System Technical Journal.

[2]  K. Aoki,et al.  New approximate optimization method for distribution system planning , 1990 .

[3]  Debasis Mitra,et al.  Effective bandwidth of general Markovian traffic sources and admission control of high speed networks , 1993, TNET.

[4]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[5]  W.R. Lachs,et al.  Uncertainty in electricity supply controlled by energy storage , 1995, Proceedings 1995 International Conference on Energy Management and Power Delivery EMPD '95.

[6]  T. V. Lakshman,et al.  Fundamental Bounds and Approximations for ATM Multiplexers with Applications to Video Teleconferencing , 1995, IEEE J. Sel. Areas Commun..

[7]  A. Winsor.  Sampling techniques. , 2000, Nursing times.

[8]  Ward Whitt,et al.  An Introduction to Stochastic-Process Limits and their Application to Queues , 2002 .

[9]  Guido Appenzeller,et al.  Sizing router buffers , 2004, SIGCOMM '04.

[10]  Willett Kempton,et al.  Vehicle-to-grid power fundamentals: Calculating capacity and net revenue , 2005 .

[11]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[12]  M.M.A. Salama,et al.  An integrated distributed generation optimization model for distribution system planning , 2005, IEEE Transactions on Power Systems.

[13]  A. V. Meier.  Electric power systems : a conceptual introduction , 2006 .

[14]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[15]  Sandip Deshmukh,et al.  Modeling of hybrid renewable energy systems , 2008 .

[16]  Santanu Bandyopadhyay,et al.  Application of design space methodology for optimum sizing of wind–battery systems , 2009 .

[17]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[18]  Ian Beausoleil-Morrison,et al.  Synthetically derived profiles for representing occupant-driven electric loads in Canadian housing , 2009 .

[19]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[20]  K. Ponnambalam,et al.  Comparison of methods for battery capacity design in renewable energy systems for constant demand and uncertain supply , 2010, 2010 7th International Conference on the European Energy Market.

[21]  J. Apt,et al.  Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization , 2010 .

[22]  Walter J. Culver,et al.  High-Value Energy Storage for the Grid: A Multi-Dimensional Look , 2010 .

[23]  Catherine Rosenberg,et al.  How internet concepts and technologies can help green and smarten the electrical grid , 2011, CCRV.

[24]  Catherine Rosenberg,et al.  Markovian models for home electricity consumption , 2011, GreenNets '11.