Factoring the Cycle Aging Cost of Batteries Participating in Electricity Markets

When participating in electricity markets, owners of battery energy storage systems must bid in such a way that their revenues will at least cover their true cost of operation. Since cycle aging of battery cells represents a substantial part of this operating cost, the cost of battery degradation must be factored in these bids. However, existing models of battery degradation either do not fit market clearing software or do not reflect the actual battery aging mechanism. In this paper we model battery cycle aging using a piecewise linear cost function, an approach that provides a close approximation of the cycle aging mechanism of electrochemical batteries and can be incorporated easily into existing market dispatch programs. By defining the marginal aging cost of each battery cycle, we can assess the actual operating profitability of batteries. A case study demonstrates the effectiveness of the proposed model in maximizing the operating profit of a battery energy storage system taking part in the ISO New England energy and reserve markets.

[1]  Richard D. Braatz,et al.  Modeling and Simulation of Lithium-Ion Batteries from a Systems Engineering Perspective , 2010 .

[2]  Daniel S. Kirschen,et al.  Modeling of Lithium-Ion Battery Degradation for Cell Life Assessment , 2018, IEEE Transactions on Smart Grid.

[3]  D. Benasciutti,et al.  Spectral methods for lifetime prediction under wide-band stationary random processes , 2005 .

[4]  Dirk Uwe Sauer,et al.  Modeling mechanical degradation in lithium ion batteries during cycling: Solid electrolyte interphase fracture , 2015 .

[5]  Julian de Hoog,et al.  A Multi-Factor Battery Cycle Life Prediction Methodology for Optimal Battery Management , 2015, e-Energy.

[6]  Hamed Mohsenian-Rad.  Optimal Bidding, Scheduling, and Deployment of Battery Systems in California Day-Ahead Energy Market , 2016, IEEE Transactions on Power Systems.

[7]  B. Dunn,et al.  Electrical Energy Storage for the Grid: A Battery of Choices , 2011, Science.

[8]  Daniel S. Kirschen,et al.  Near-Optimal Method for Siting and Sizing of Distributed Storage in a Transmission Network , 2015, IEEE Transactions on Power Systems.

[9]  M. Verbrugge,et al.  Cycle-life model for graphite-LiFePO 4 cells , 2011 .

[10]  Goran Andersson,et al.  Defining a degradation cost function for optimal control of a battery energy storage system , 2013, 2013 IEEE Grenoble Conference.

[11]  Daniel S. Kirschen,et al.  Stochastic Multistage Coplanning of Transmission Expansion and Energy Storage , 2017, IEEE Transactions on Power Systems.

[12]  Andrew Wirth,et al.  Optimal operation of energy storage systems considering forecasts and battery degradation , 2017, 2017 IEEE Power & Energy Society General Meeting.

[13]  Sanna Syri,et al.  Electrical energy storage systems: A comparative life cycle cost analysis , 2015 .

[14]  Jianhui Wang,et al.  Further Discussions on Sufficient Conditions for Exact Relaxation of Complementarity Constraints for Storage-Concerned Economic Dispatch , 2015, ArXiv.

[15]  Yang-Tse Cheng,et al.  Crack Pattern Formation in Thin Film Lithium-Ion Battery Electrodes , 2011 .

[16]  Duong Tran,et al.  Energy Management for Lifetime Extension of Energy Storage System in Micro-Grid Applications , 2013, IEEE Transactions on Smart Grid.

[17]  M. Verbrugge,et al.  Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide + spinel manganese oxide positives: Part 1, aging mechanisms and life estimation , 2014 .

[18]  Dale T. Bradshaw,et al.  DOE/EPRI Electricity Storage Handbook in Collaboration with NRECA , 2016 .

[19]  Chongqing Kang,et al.  Optimal Bidding Strategy of Battery Storage in Power Markets Considering Performance-Based Regulation and Battery Cycle Life , 2016, IEEE Transactions on Smart Grid.

[20]  Atilla Incecik,et al.  Review and application of Rainflow residue processing techniques for accurate fatigue damage estimation , 2016 .

[21]  R. Byrne.  Estimating the Maximum Potential Revenue for Grid Connected Electricity Storage: Arbitrage and Regulation , 2012 .

[22]  C. Amzallag,et al.  Standardization of the rainflow counting method for fatigue analysis , 1994 .

[23]  C. Delacourt,et al.  Calendar aging of a graphite/LiFePO4 cell , 2012 .

[24]  Cesar A. Silva-Monroy,et al.  Ensuring Profitability of Energy Storage , 2017, IEEE Transactions on Power Systems.

[25]  J. Watson,et al.  Assessing the economic value of co-optimized grid-scale energy storage investments in supporting high renewable portfolio standards , 2016 .

[26]  Javier Contreras,et al.  Unit Commitment With Ideal and Generic Energy Storage Units , 2014, IEEE Transactions on Power Systems.

[27]  Alan Millner,et al.  Modeling Lithium Ion battery degradation in electric vehicles , 2010, 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply.

[28]  M. Wohlfahrt‐Mehrens,et al.  Ageing mechanisms in lithium-ion batteries , 2005 .

[29]  D. Sauer,et al.  Calendar and cycle life study of Li(NiMnCo)O2-based 18650 lithium-ion batteries , 2014 .

[30]  Ali Fatemi,et al.  Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials , 1998 .

[31]  Michael Cw Kintner-Meyer,et al.  Energy Storage for Power Systems Applications: A Regional Assessment for the Northwest Power Pool (NWPP) , 2010 .

[32]  P. Ruetschi.  Aging mechanisms and service life of lead–acid batteries , 2004 .