Computational Management Science

, Volume 13, Issue 1, pp 5–27 | Cite as

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

  • Pedro Crespo Del Granado
  • Stein W. Wallace
  • Zhan Pang
Original Paper


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.


Smart grid Wind energy Energy storage Uncertainty Valuation Stochastic programming 



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.


  1. Ahlert K, van Dinther C (2009) Sensitivity analysis of the economic benefits from electricity storage at the end consumer level. In: PowerTech, 2009 IEEE Bucharest, pp 1–8Google Scholar
  2. Banham-Hall D, Taylor G, Smith C, Irving M (2012) Flow batteries for enhancing wind power integration. IEEE Trans Power Syst 27(3):1690–1697CrossRefGoogle Scholar
  3. Brown P, Lopes JP, Matos M (2008) Optimization of pumped storage capacity in an isolated power system with large renewable penetration. IEEE Trans Power Syst 23(2):523–531Google Scholar
  4. Bruninx K, Delarue E, Dhaeseleer W (2014) A modeling framework for the integration of intermittent renewables: stochastic unit commitment. TME Working paper, WP EN2014-1, KU Leuven Energy InstituteGoogle Scholar
  5. Carbon Connect (2012) Distributed generation: from Cinderella to centre stage. Technical report, Policy ConnectGoogle Scholar
  6. Cardoso G, Stadler M, Siddiqui A, Marnay C, DeForest N, Barbosa-Pvoa A, Ferro P (2013) Microgrid reliability modeling and battery scheduling using stochastic linear programming. Electr Power Syst Res 103:61–69CrossRefGoogle Scholar
  7. Castronuovo ED, Lopes JAP (2004) Optimal operation and hydro storage sizing of a wind hydro power plant. Int J Electr Power Energy Syst 26(10):771–778CrossRefGoogle Scholar
  8. Conejo AJ, Carrin M, Morales JM (2010) Decision making under uncertainty in electricity markets., International series in operations research and management science, Springer, New YorkGoogle Scholar
  9. Crespo Del Granado P, Wallace SW, Pang Z (2014) The value of electricity storage in domestic homes: a smart grid perspective. Energy Syst 5(2):211–232CrossRefGoogle Scholar
  10. DECC (2013) UK renewable energy roadmap update 2013. Technical report, Department of Energy and Climate ChangeGoogle Scholar
  11. ELEXON-Ltd (2010–2011) The balancing mechanism reporting system, the new electricity trading arrangements, electricity historic prices. [Online]
  12. EPRI (2010) Electricity energy storage technology options: a white paper primer on applications, costs, and benefits. Technical report, Electric Power Research InstituteGoogle Scholar
  13. European Commission (2014) A policy framework for climate and energy in the period from 2020 to 2030. Technical report, Eurpean UnionGoogle Scholar
  14. Eyer J, Corey G (2010) Energy storage for the electricity grid: Benefits and market potential assessment guide. Technical report, Sandia National LaboratoriesGoogle Scholar
  15. Garcia-Gonzalez J, Ruiz de la Muela R, Santos L, Gonzalez A (2008) Stochastic joint optimization of wind generation and pumped-storage units in an electricity market. IEEE Trans Power Syst 23(2):460–468CrossRefGoogle Scholar
  16. Hanna R, Kleissl J, Nottrott A, Ferry M (2014) Energy dispatch schedule optimization for demand charge reduction using a photovoltaic-battery storage system with solar forecasting. Solar Energy 103:269–287CrossRefGoogle Scholar
  17. Harsha P, Dahleh M (2015) Optimal management and sizing of energy storage under dynamic pricing for the efficient integration of renewable energy. IEEE Trans Power Syst 30(3):1164–1181CrossRefGoogle Scholar
  18. Hatziargyriou N, Asano H, Iravani R, Marnay C (2007) Microgrids. Power Energy Mag IEEE 5(4):78–94CrossRefGoogle Scholar
  19. Hazelrigg Weather Station (2006–2011) Historical wind speed data sets on half hour basis. Lancaster University Enviromental Center.
  20. He X, Delarue E, D’Haeseleer W, Glachant J-M (2011) A novel business model for aggregating the values of electricity storage. Energy Policy 39(3):1575–1585CrossRefGoogle Scholar
  21. Hittinger E, Whitacre J, Apt J (2012) What properties of grid energy storage are most valuable? J Power Sources 206:436–449CrossRefGoogle Scholar
  22. IBM (2012) ILOG CPLEX (version 12). [Online] [Software]
  23. Kear G, Shah AA, Walsh FC (2012) Development of the all-vanadium redox flow battery for energy storage: a review of technological, financial and policy aspects. Int J Energy Res 36(11):1105–1120CrossRefGoogle Scholar
  24. Kim JH, Powell WB (2011) Optimal energy commitments with storage and intermittent supply. Oper Res 59(6):1347–1360CrossRefGoogle Scholar
  25. King AJ, Wallace SW (2012) Modeling with stochastic programming. Springer, Springer Series in Operations Research and Financial EngineeringGoogle Scholar
  26. Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Electr Power Energy Syst 25(8):599–606CrossRefGoogle Scholar
  27. Küchler C (2009) Stability, approximation, and decomposition in two- and multistage stochastic programming., Stochastic programming, Springer, New YorkGoogle Scholar
  28. Lancaster University (2011) Lancaster University carbon management plan 2009–2012. Technical report, Lancaster University Facilities DepartmentGoogle Scholar
  29. Mathworks (2012) MATLAB R2012b (version 8). [Online] [Software]
  30. Mishra A, Irwin D, Shenoy P, Kurose J, Zhu T (2012) Smartcharge: cutting the electricity bill in smart homes with energy storage. In: Proceedings of the 3rd international conference on future energy systems, e-Energy, pp 29:1–29:10. ACMGoogle Scholar
  31. Mokrian P, Stephen M (2006) A stochastic programming framework for the valuation of electricity storage. The 26th USAEE/IAEE North American Conference. Michigan, USA, Ann Arbor, pp 24–26Google Scholar
  32. Moreno R, Moreira R, Strbac G (2015) A MILP model for optimising multi-service portfolios of distributed energy storage. Appl Energy 137:554–566CrossRefGoogle Scholar
  33. Nyamdash B, Denny E, O’Malley M (2010) The viability of balancing wind generation with large scale energy storage. Energy Policy 38(11):7200–7208CrossRefGoogle Scholar
  34. Prudent Energy group (2011) Storage for a sustaniable future, product brochure. [Online]
  35. Roberts B, Sandberg C (2011) The role of energy storage in development of smart grids. Proc IEEE 99(6):1139–1144CrossRefGoogle Scholar
  36. Schütz P, Tomasgard A (2011) The impact of flexibility on operational supply chain planning. Int J Prod Econ 134(2):300–311. ‘Robust Supply Chain Management’Google Scholar
  37. Scott W, Powell W (2012) Approximate dynamic programming for energy storage with new results on instrumental variables and projected bellman errors. Working paper, Princeton UniversityGoogle Scholar
  38. Sharma KC, Jain P, Bhakar R (2013) Wind power scenario generation and reduction in stochastic programming framework. Electr Power Compon Syst 41(3):271–285CrossRefGoogle Scholar
  39. 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(2):269–277CrossRefGoogle Scholar
  40. Sundararagavan S, Baker E (2012) Evaluating energy storage technologies for wind power integration. Solar Energy 86(9):2707–2717CrossRefGoogle Scholar
  41. Teh NJ, Goujon G, Bortuzzo G, Rhodes A (2011) UK smart grid capabilities development programme. Technical report,Energy Generation and Supply, Knowledge Transfer NetworkGoogle Scholar
  42. Torres J, Garca A, Blas MD, Francisco AD (2005) Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar Energy 79(1):65–77CrossRefGoogle Scholar
  43. United States Department of Energy (2013) DOE international energy storage database. [Online]
  44. Vespucci MT, Maggioni F, Bertocchi MI, Innorta M (2010) A stochastic model for the daily coordination of pumped storage hydro plants and wind power plants. Ann Oper Res 193(1):91–105Google Scholar
  45. Walawalkar R, Apt J, Mancini R (2007) Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy 35(4):2558–2568CrossRefGoogle Scholar
  46. Weber C, Meibom P, Barth R, Brand H (2009) Wilmar: a stochastic programming tool to analyze the large-scale integration of wind energy. In: Kallrath J, Pardalos P, Rebennack S, Scheidt M (eds) Optimization in the energy industry, energy systems. Springer, Berlin, pp 437–458CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Energy Science CenterETH ZürichZurichSwitzerland
  2. 2.Department of Management ScienceLancaster University Management SchoolLancasterUK
  3. 3.Department of Business and Management ScienceNorwegian School of EconomicsBergenNorway

Personalised recommendations