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The impact of wind uncertainty on the strategic valuation of distributed electricity storage

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Abstract

The intermittent nature of wind energy generation has introduced a new degree of uncertainty to the tactical planning of energy systems. Short-term energy balancing decisions are no longer (fully) known, and it is this lack of knowledge that causes the need for strategic thinking. But despite this observation, strategic models are rarely set in an uncertain environment. And even if they are, the approach used is often inappropriate, based on some variant of scenario analysis—what-if analysis. In this paper we develop a deterministic strategic model for the valuation of electricity storage (a battery), and ask: “Though leaving out wind speed uncertainty clearly is a simplification, does it really matter for the valuation of storage?”. We answer this question by formulating a stochastic programming model, and compare its valuation to that of its deterministic counterpart. Both models capture the arbitrage value of storage, but only the stochastic model captures the battery value stemming from wind speed uncertainty. Is the difference important? The model is tested on a case from Lancaster University’s campus energy system where a wind turbine is installed. From our analysis, we conclude that considering wind speed uncertainty can increase the estimated value of storage with up to 50 % relative to a deterministic estimate. However, we also observe cases where wind speed uncertainty is insignificant for storage valuation.

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Notes

  1. A CHP is a gas driven energy unit that generates electricity and heat simultaneously.

  2. Wind turbine Model E-70 manufactured by ENERCON GmbH; further technical information available at http://www.enercon.de (accessed on July 31, 2013).

  3. \(\xi _{{wind}}(v)=0.0498v^5 -2.5155v^4 +44.91v^3 -336.68v^2 +1165.9v-1480.5\).

  4. Prudent Energy’s standard VRB-ESS MW-Class module. This 250 KW battery is designed for the support of renewable energy integration for micro grids.

  5. Note that the solution lives on a scenario tree like the one in Fig. 3.

  6. The wind speeds data stem from an anemometer height lower than the wind turbine height. To account for this difference, we follow a process noted in Sharma et al. (2013).

  7. Due to the complexities associated with the calculations of the multi-stage stochastic setting, the pie charts numbers come from the expected cases. The idea is to simply illustrate and qualitatively note that ‘wind value %’ causes uncertainty on storage decisions.

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Acknowledgments

We thank the Facilities Department at Lancaster University for providing information on the campus energy system as well as electricity demand data. Likewise, to the Lancaster Environmental Center for sharing high frequency data on historical wind speeds. Lastly, our acknowledgements to the Department of Management Science at Lancaster University and the Department of Business and Management Science at The Norwegian School of Economics for facilitating research visits.

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Authors and Affiliations

  1. Energy Science Center, ETH Zürich, Sonneggstrassse 28, 8092, Zurich, Switzerland

    Pedro Crespo Del Granado

  2. Department of Management Science, Lancaster University Management School, Lancaster, LA1 4YX, UK

    Pedro Crespo Del Granado & Zhan Pang

  3. Department of Business and Management Science, Norwegian School of Economics, 5045, Bergen, Norway

    Stein W. Wallace

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  1. Pedro Crespo Del Granado
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Correspondence to Pedro Crespo Del Granado.

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Crespo Del Granado, P., Wallace, S.W. & Pang, Z. The impact of wind uncertainty on the strategic valuation of distributed electricity storage. Comput Manag Sci 13, 5–27 (2016). https://doi.org/10.1007/s10287-015-0235-0

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