A Convex Cycle-based Degradation Model for Battery Energy Storage Planning and Operation

A vital aspect in energy storage planning and operation is to accurately model its operational cost, which mainly comes from the battery cell degradation. Battery degradation can be viewed as a complex material fatigue process that is based on stress cycles. Rainflow algorithm is a popular way for cycle identification in material fatigue process, and has been extensively used in battery degradation assessment. However, the rainflow algorithm does not have a closed form, which is the major difficulty to include it in optimization. In this paper, we prove the rainflow cycle-based cost is convex. Convexity enables the proposed degradation model to be incorporated in different battery optimization problems with a guarantee of the solution quality. We provide a subgradient algorithm to solve the problem. A case study on PJM regulation market demonstrates the effectiveness of the proposed degradation model in maximizing the battery operating profits as well as extending its lifetime.

[1]  Yuguang Fang,et al.  Electricity Cost Saving Strategy in Data Centers by Using Energy Storage , 2013, IEEE Transactions on Parallel and Distributed Systems.

[2]  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.

[3]  Di Wang,et al.  Leveraging energy storage to optimize data center electricity cost in emerging power markets , 2016, e-Energy.

[4]  I. Rychlik A new definition of the rainflow cycle counting method , 1987 .

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

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

[7]  B. Nykvist,et al.  Rapidly falling costs of battery packs for electric vehicles , 2015 .

[8]  Enzo Sauma,et al.  Unit commitment with ideal and generic energy storage units , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[9]  Mahera Musallam,et al.  An Efficient Implementation of the Rainflow Counting Algorithm for Life Consumption Estimation , 2012, IEEE Transactions on Reliability.

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

[11]  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.

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

[13]  Cesar A. Silva-Monroy,et al.  A comparison of policies on the participation of storage in U.S. frequency regulation markets , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[14]  Pan Li,et al.  An optimal treatment assignment strategy to evaluate demand response effect , 2016, 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[15]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

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

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

[18]  Neil Genzlinger A. and Q , 2006 .

[19]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[20]  Stephen P. Boyd,et al.  Subgradient Methods , 2007 .

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

[22]  Na Li,et al.  Optimal demand response based on utility maximization in power networks , 2011, 2011 IEEE Power and Energy Society General Meeting.

[23]  Henk Jan Bergveld,et al.  Battery Management Systems: Accurate State-of-Charge Indication for Battery-Powered Applications , 2008 .

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

[25]  W. Marsden I and J , 2012 .

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

[27]  Darrell F. Socie,et al.  Simple rainflow counting algorithms , 1982 .

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

[29]  Josep M. Guerrero,et al.  Capacity Optimization of Renewable Energy Sources and Battery Storage in an Autonomous Telecommunication Facility , 2014, IEEE Transactions on Sustainable Energy.

[30]  Di Wang,et al.  Using Battery Storage for Peak Shaving and Frequency Regulation: Joint Optimization for Superlinear Gains , 2017, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[31]  K. Poolla,et al.  The role of co-located storage for wind power producers in conventional electricity markets , 2011, Proceedings of the 2011 American Control Conference.

[32]  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.