On the value of storage at consumer locations

We study the economic value of energy storage operated by a consumer who faces fluctuating electricity prices and seeks to reduce its energy costs. The value of storage is defined as the consumer's net benefit obtained by optimally operating the storage. We formulate the operation problem as a dynamic program. For a general setting with random electricity prices and stochastic demand, we show that by solving a sequence of (deterministic) convex optimization problems one can obtain an optimal operation policy as well as the value of storage. For an important special case where the consumer is faced with deterministic time-variant prices and (deterministic) inelastic demand, we propose a simple greedy algorithm that simultaneously computes the optimal operation policy and the economic value of a finite-capacity electric storage. We employ the proposed algorithm to numerically explore the value of storage under different pricing mechanisms.

[1]  Pedram Mokrian,et al.  A Stochastic Programming Framework for the Valuation of Electricity Storage , 2022 .

[2]  Peng Xu,et al.  Automated Critical Peak Pricing Field Tests: Program Description and Results , 2006 .

[3]  T. Jenkin,et al.  Opportunities for Electricity Storage in Deregulating Markets , 1999 .

[4]  R. Rajagopal,et al.  Optimal electric energy storage operation , 2012, 2012 IEEE Power and Energy Society General Meeting.

[5]  R. Sioshansi Welfare Impacts of Electricity Storage and the Implications of Ownership Structure , 2010 .

[6]  Mehdi Etezadi-Amoli,et al.  Rapid-Charge Electric-Vehicle Stations , 2010, IEEE Transactions on Power Delivery.

[7]  Thomas N. Taylor,et al.  24/7 Hourly Response to Electricity Real-Time Pricing with up to Eight Summers of Experience , 2005 .

[8]  C. Goldman,et al.  Demand Response from Day-Ahead Hourly Pricing for Large Customers , 2006 .

[9]  Anand Sivasubramaniam,et al.  Optimal power cost management using stored energy in data centers , 2011, PERV.

[10]  Han-I Su,et al.  Modeling and analysis of the role of fast-response energy storage in the smart grid , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[11]  S. Borenstein,et al.  Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets , 2002 .

[12]  Leandros Tassiulas,et al.  Optimal energy storage control policies for the smart power grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[13]  Yashodhan Kanoria,et al.  Distributed storage for intermittent energy sources: Control design and performance limits , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[14]  Jay F. Whitacre,et al.  The economics of using plug-in hybrid electric vehicle battery packs for grid storage , 2010 .

[15]  Jean C. Walrand,et al.  Optimal demand response with energy storage management , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).

[16]  Robert B. Wilson,et al.  Research Paper Series Graduate School of Business Stanford University Architecture of Power Markets Architecture of Power Markets 1 , 2022 .

[17]  Paul Denholm,et al.  A Dynamic Programming Approach to Estimate the Capacity Value of Energy Storage , 2014, IEEE Transactions on Power Systems.

[18]  Laurent Massoulié,et al.  Optimal Control of End-User Energy Storage , 2012, IEEE Transactions on Smart Grid.

[19]  Yuguang Fang,et al.  Cutting Down Electricity Cost in Internet Data Centers by Using Energy Storage , 2011, 2011 IEEE Global Telecommunications Conference - GLOBECOM 2011.