Optimal Hour-Ahead Bidding in the Real-Time Electricity Market with Battery Storage Using Approximate Dynamic Programming

Published Online:https://doi.org/10.1287/ijoc.2015.0640

References

  • Barnhart CJ, Dale M, Brandt AR, Benson SM (2013) The energetic implications of curtailing versus storing solar- and wind-generated electricity. Energy Environ. Sci. 6(10):2804–2810.CrossrefGoogle Scholar
  • Bellman RE (1957) Dynamic Programming (Princeton University Press, Princeton, NJ).Google Scholar
  • Bertsekas DP, Tsitsiklis JN (1996) Neuro-Dynamic Programming (Athena Scientific, Belmont, MA).Google Scholar
  • Breiman L (1992) Probability (Society of Industrial and Applied Mathematics, Philadelphia).CrossrefGoogle Scholar
  • Byrne RH, Silva-Monroy CA (2012) Estimating the maximum potential revenue for grid connected electricity storage: Arbitrage and regulation. Technical report SAND2012-3863, Sandia National Laboratories, Albuquerque, NM.Google Scholar
  • Carmona R, Coulon M (2014) A survey of commodity markets and structural models for electricity prices. Benth FE, Kholodnyi VA, Laurence P, eds. Quantitative Energy Finance (Springer, New York), 41–83.CrossrefGoogle Scholar
  • Carmona R, Ludkovski M (2010) Valuation of energy storage: An optimal switching approach. Quant. Finance 10(4):359–374.CrossrefGoogle Scholar
  • Cartea Á, Figueroa MG (2005) Pricing in electricity markets: A mean reverting jump diffusion model with seasonality. Appl. Math. Finance 12(4):313–335.CrossrefGoogle Scholar
  • Conejo AJ, Nogales FJ, Arroyo JM (2002) Price-taker bidding strategy under price uncertainty. IEEE Trans. Power Systems 17(4):1081–1088.CrossrefGoogle Scholar
  • Coulon M, Powell WB, Sircar R (2013) A model for hedging load and price risk in the Texas electricity market. Energy Econom. 40:976–988.CrossrefGoogle Scholar
  • David AK (1993) Competitive bidding in electricity supply. Generation Transm. Distrib. IEE Proc. C 140(5):421–426.CrossrefGoogle Scholar
  • Eydeland A, Wolyniec K (2003) Energy and Power Risk Management (Wiley, Hoboken, NJ).Google Scholar
  • George AP, Powell WB (2006) Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming. Machine Learn. 65(1):167–198.CrossrefGoogle Scholar
  • Godfrey GA, Powell WB (2001) An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution. Management Sci. 47(8):1101–1112.LinkGoogle Scholar
  • Greenblatt JB, Succar S, Denkenberger DC, Williams RH, Socolow RH (2007) Baseload wind energy: Modeling the competition between gas turbines and compressed air energy storage for supplemental generation. Energy Policy 35(3):1474–1492.CrossrefGoogle Scholar
  • Gross G, Finlay D (2000) Generation supply bidding in perfectly competitive electricity markets. Comput. Math. Organ. Theory 6(1):83–98.CrossrefGoogle Scholar
  • Harris C (2011) Electricity Markets: Pricing, Structures and Economics (John Wiley & Sons, Chichester, UK).Google Scholar
  • Jiang DR, Powell WB (2015) An approximate dynamic programming algorithm for monotone value functions. Available at http://arxiv.org/abs/1401.1590.Google Scholar
  • Kim JH, Powell WB (2011) Optimal energy commitments with storage and intermittent supply. Oper. Res. 59(6):1347–1360.LinkGoogle Scholar
  • Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.CrossrefGoogle Scholar
  • Lai G, Margot F, Secomandi N (2010) An approximate dynamic programming approach to benchmark practice-based heuristics for natural gas storage valuation. Oper. Res. 58(3):564–582.LinkGoogle Scholar
  • Löhndorf N, Minner S (2010) Optimal day-ahead trading and storage of renewable energies: An approximate dynamic programming approach. Energy Systems 1(1):61–77.CrossrefGoogle Scholar
  • Löhndorf N, Wozabal D, Minner S (2013) Optimizing trading decisions for hydro storage systems using approximate dual dynamic programming. Oper. Res. 61(4):810–823.LinkGoogle Scholar
  • Nandalal KDW, Bogardi JJ (2007) Dynamic Programming Based Operation of Reservoirs: Applicability and Limits (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Nascimento JM, Powell WB (2009) An optimal approximate dynamic programming algorithm for the lagged asset acquisition problem. Math. Oper. Res. 34(1):210–237.LinkGoogle Scholar
  • Paatero JV, Lund PD (2005) Effect of energy storage on variations in wind power. Wind Energy 8(4):421–441.CrossrefGoogle Scholar
  • Papadaki KP, Powell WB (2003) An adaptive dynamic programming algorithm for a stochastic multiproduct batch dispatch problem. Naval Res. Logist. 50(7):742–769.CrossrefGoogle Scholar
  • PJM Manual 11: Energy and Ancillary Services Market Operations, http://www.pjm.com/~/media/documents/manuals/m11.ashx, April 2015.Google Scholar
  • Powell WB (2011) Approximate Dynamic Programming: Solving the Curses of Dimensionality, 2nd ed. (Wiley, Hoboken, NJ).CrossrefGoogle Scholar
  • Powell WB, Ruszczyński A, Topaloglu H (2004) Learning algorithms for separable approximations of discrete stochastic optimization problems. Math. Oper. Res. 29(4):814–836.LinkGoogle Scholar
  • Schwartz ES (1997) The stochastic behavior of commodity prices: Implications for valuation and hedging. J. Finance 52(3): 923–973.CrossrefGoogle Scholar
  • Secomandi N (2010) Optimal commodity trading with a capacitated storage asset. Management Sci. 56(3):449–467.LinkGoogle Scholar
  • Shahidehpour M, Yamin H, Li Z (2002) Market Operations in Electric Power Systems (John Wiley & Sons, New York).CrossrefGoogle Scholar
  • Sioshansi R (2011) Increasing the value of wind with energy storage. Energy J. 32(2):1–29.CrossrefGoogle Scholar
  • Sioshansi R, Denholm P, Jenkin T (2011) A comparative analysis of the value of pure and hybrid electricity storage. Energy Econom. 33(1):56–66.CrossrefGoogle Scholar
  • Sioshansi R, Denholm P, Jenkin T, Weiss J (2009) Estimating the value of electricity storage in PJM: Arbitrage and some welfare effects. Energy Econom. 31(2):269–277.CrossrefGoogle Scholar
  • Thompson M, Davison M, Rasmussen H (2009) Natural gas storage valuation and optimization: A real options application. Naval Res. Logist. 56(3):226–238.CrossrefGoogle Scholar
  • Topaloglu H, Powell WB (2003) An algorithm for approximating piecewise linear concave functions from sample gradients. Oper. Res. Lett. 31(1):66–76.CrossrefGoogle Scholar
  • Tsitsiklis JN (1994) Asynchronous stochastic approximation and Q-learning. Machine Learn. 16(3):185–202.CrossrefGoogle Scholar
  • Walawalkar R, Apt J, Mancini R (2007) Economics of electric energy storage for energy arbitrage and regulation in New York. Energy Policy 35(4):2558–2568.CrossrefGoogle Scholar
  • Wen F, David AK (2000) Strategic bidding in competitive electricity markets: A literature survey. Power Engrg. Soc. Summer Meeting, Vol. 4 (IEEE, New York), 2168–2173.Google Scholar
  • Yang Z, Zhang J, Kintner-Meyer MCW, Lu X, Choi D, Lemmon JP, Liu J (2011) Electrochemical energy storage for green grid. Chemical Rev. 111(5):3577–613.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.