Seasonal Energy Storage Operations with Limited Flexibility

The value of seasonal energy storage depends on how the firm best operates the storage to capture the seasonal price spread. Energy storage operations typically face limited operational flexibility characterized by the speed of storing and releasing energy. A widely used practice-based heuristic, the rolling intrinsic (RI) policy, generally performs well, but can significantly under-perform in some cases. In this paper, we aim to understand the gap between the RI policy and the optimal policy, and design improved heuristic policies to close or reduce this gap. A new heuristic policy, the “price-adjusted rolling intrinsic (PARI) policy,” is developed based on theoretical analysis of the value of storage options. This heuristic adjusts prices before applying the RI policy, and the adjusted prices inform the RI policy about the values of various storage options. Our numerical experiments show that the PARI policy is especially capable of recovering high value losses of the RI policy. For the instances where the RI policy loses more than 4% of the optimal storage value, the PARI policy on average is able to recover more than 90% of the value loss.

[1]  Patrick Jaillet,et al.  Valuation of Commodity-Based Swing Options , 2004, Manag. Sci..

[2]  S. Hodges,et al.  The Value of a Storage Facility , 2004 .

[3]  Alan Scheller-Wolf,et al.  Valuation of Storage at a Liquefied Natural Gas Terminal , 2011, Oper. Res..

[4]  M. Manoliu,et al.  Energy futures prices: term structure models with Kalman filter estimation , 2002 .

[5]  Zhuliang Chen,et al.  A Semi-Lagrangian Approach for Natural Gas Storage Valuation and Optimal Operation , 2007, SIAM J. Sci. Comput..

[6]  A. Charnes,et al.  Decision and horizon rules for stochastic planning problems : a linear example , 1966 .

[7]  W. Prager.  On Warehousing Problems , 1957 .

[8]  D. Hunter.  Valuation of a natural gas storage facility , 2008 .

[9]  L. Clewlow,et al.  Energy Derivatives: Pricing and Risk Management , 2000 .

[10]  Richard Bellman,et al.  On the Theory of Dynamic Programming---A Warehousing Problem , 1956 .

[11]  Mihaela Manoliu.  STORAGE OPTIONS VALUATION USING MULTILEVEL TREES AND CALENDAR SPREADS , 2004 .

[12]  Eduardo S. Schwartz,et al.  Investment Under Uncertainty. , 1994 .

[13]  Nicola Secomandi,et al.  An Approximate Dynamic Programming Approach to Benchmark Practice-Based Heuristics for Natural Gas Storage Valuation , 2010, Oper. Res..

[14]  D. Duffie.  Dynamic Asset Pricing Theory , 1992 .

[15]  A. Eydeland.  Energy and Power Risk Management , 2002 .

[16]  C. E. Clark.  The Greatest of a Finite Set of Random Variables , 1961 .

[17]  A. Charnes,et al.  Generalizations of the Warehousing Model , 1955 .

[18]  Hong Chen,et al.  Optimal Control and Equilibrium Behavior of Production-Inventory Systems , 2010, Manag. Sci..

[19]  Yun Li,et al.  Natural Gas Storage Valuation , 2009 .

[20]  Stuart E. Dreyfus.  An Analytic Solution of the Warehouse Problem , 1957 .

[21]  Nicola Secomandi,et al.  Optimal Commodity Trading with a Capacitated Storage Asset , 2010, Manag. Sci..

[22]  R. Carmona,et al.  Valuation of energy storage: an optimal switching approach , 2010 .

[23]  Genaro J. Gutierrez,et al.  Integrating Commodity Markets in the Optimal Procurement Policies of a Stochastic Inventory System , 2012 .

[24]  Alexander Boogert,et al.  Gas Storage Valuation Using a Monte Carlo Method , 2008 .

[25]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[26]  Kenneth A. Froot,et al.  Risk Management, Capital Budgeting and Capital Structure Policy for Financial Institutions: An Integrated Approach , 1996 .

[27]  Y. Feng,et al.  Value, trading strategies and financial investment of natuarl gas storage assets , 2008 .

[28]  J. E. Jackson,et al.  Statistical Factor Analysis and Related Methods: Theory and Applications , 1995 .

[29]  M. Davison,et al.  Natural gas storage valuation and optimization: A real options application , 2009 .