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A dynamic programming model of energy storage and transformer deployments to relieve distribution constraints

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Abstract

We introduce a stochastic dynamic programming (SDP) model that co-optimizes multiple uses of distributed energy storage, including energy and ancillary service sales, backup capacity, and transformer loading relief, while accounting for market and system uncertainty. We propose an approximation technique to efficiently solve the SDP. We also use a case study with high residential loads to demonstrate that a deployment consisting of both storage and transformer upgrades decreases costs and increases value relative to a transformer-only deployment.

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Notes

  1. In keeping with our convention that variables with the subscript \(t\) are stochastic before hour \(t\) and become known at hour \(t\), we put a \(t+1\) subscript on these random samples. This is because the random samples are computed based on a realization of \(\tilde{\omega }_{t+1}\).

  2. Since this testing is still preliminary, results are not yet publicly available. The experiments cycle batteries repeatedly and under different temperature conditions to determine their capacities and aging characteristics.

  3. We only model battery capacities with starting energy capacities in 5 kWh increments. A deployment with a 25 kVA transformer and a battery with a starting nominal capacity of 5 kWh results in an expected transformer life of 18.4 years. A 10 kWh battery results in a 21.3 year transformer life.

References

  • Collins MM, Mader GH (1983) The timing of EV recharging and its effect on utilities. IEEE Trans Vehicular Technol 32:90–97

    Article  Google Scholar 

  • DeSieno CF, Stine LL (1965) A probability method for determining the reliability of electric power systems. IEEE Trans Reliabil 14:30–35

    Article  Google Scholar 

  • Gong Q, Midlam-Mohler S, Marano V, Rizzoni G, (2011) PEV charging impact on residential distribution transformer life. In: 2011 IEEE Energytech. Institute of Electrical and Electronics Engineers, Cleveland

  • Graves F, Jenkin T, Murphy D (1999) Opportunities for electricity storage in deregulating markets. Electric J 12:46–56

    Article  Google Scholar 

  • Kariuki KK, Allan RN (1996) Evaluation of reliability worth and value of lost load. IEE Proc Gener Trans Distrib 143:171–180

    Article  Google Scholar 

  • Kempton W, Tomić J (2005) Vehicle-to-grid power fundamentals: calculating capacity and net revenue. J Power Sources 144:268–279

  • Kintner-Meyer MCW, Schneider KP, Pratt RG (2007) Impacts assessment of plug-in hybrid vehicles on electric utilities and regional US power grids: part 1: technical analysis. Online J EUEC 1(4)

  • Knittel CR, Roberts MR (2005) An empirical examination of restructured electricity prices. Energy Econ 27:791–817

    Article  Google Scholar 

  • Mohseni P, Stevie RG (2009) Electric vehicles: holy grail or fool’s gold. In: Power and energy society general meeting, Institute of Electrical and Electronics Engineers, Calgary, pp 1–5

  • Nascimento JM, Powell WB (2009) An optimal approximate dynamic programming algorithm for the energy dispatch problem with grid-level storage, working paper

  • Nourai A (2007) Installation of the first distributed energy storage system (DESS) at American electric power (AEP). Technical Report SAND2007-3580, Sandia National Laboratories, USA

  • Powell WB (2007) Approximate dynamic programming: solving the curses of dimensionality. Wiley-Interscience, Hoboken

    Book  Google Scholar 

  • Rockafellar RT (1970) Convex analysis. Princeton University Press, Princeton

    Book  Google Scholar 

  • Shapiro A (2003) Inference of statistical bounds for multistage stochastic programming problems. Math Methods Oper Res 58:57–68

    Article  Google Scholar 

  • Shrestha GB, Songbo Q (2010) Statistical characterization of electricity price in competitive power markets. In: 2010 IEEE 11th international conference on probabilistic methods applied to power systems (PMAPS), Institute of Electrical and Electronics Engineers, Singapore

  • Sioshansi R, Denholm P (2010) The value of plug-in hybrid electric vehicles as grid resources. Energy J 31:1–23

    Google Scholar 

  • Sioshansi R, Denholm P, Jenkin T, Weiss J (2009) Estimating the value of electricity storage in PJM: arbitrage and some welfare effects. Energy Econ 31:269–277

    Article  Google Scholar 

  • Susa D, Lehtonen M, Nordman H (2005) Dynamic thermal modelling of power transformers. IEEE Trans Power Deliv 20:197–204

    Article  Google Scholar 

  • Tomić J, Kempton W (2007) Using fleets of electric-drive vehicles for grid support. J Power Sour 168:459–468

    Article  Google Scholar 

  • Xi X, Sioshansi R, Marano V (2014) A stochastic dynamic programming model for co-optimization of distributed energy storage. Energy Syst Forthcom

Download references

Acknowledgments

The authors thank A. Sorooshian, the editors, and two anonymous reviewers for helpful discussions and suggestions and Y. Guezennec for providing useful battery aging and temperature performance data. Financial support for this work was provided by the SMART@CAR consortium. This work was also supported in part by an allocation of computing time from the Ohio Supercomputer Center. Any opinions and conclusions expressed in this paper are solely those of the authors.

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Authors and Affiliations

  1. Integrated Systems Engineering Department, The Ohio State University, Columbus, OH, USA

    Xiaomin Xi & Ramteen Sioshansi

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  1. Xiaomin Xi
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  2. Ramteen Sioshansi
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Correspondence to Ramteen Sioshansi.

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Xi, X., Sioshansi, R. A dynamic programming model of energy storage and transformer deployments to relieve distribution constraints. Comput Manag Sci 13, 119–146 (2016). https://doi.org/10.1007/s10287-014-0218-6

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  • DOI: https://doi.org/10.1007/s10287-014-0218-6

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