Control and Size Energy Storage Systems for Managing Energy Imbalance of Variable Generation Resources

This paper presents control algorithms and sizing strategies for using energy storage to manage energy imbalance for variable generation resources. The control objective is to minimize the hourly generation imbalance between the actual and the scheduled generation of wind farms. Three control algorithms are compared: 1)tracking minute-by-minute power imbalance; 2)postcompensation; and 3)precompensation. Measured data from a wind farm are used in the study. The results show that tracking minute-by-minute power imbalance achieves the best performance by keeping hourly energy imbalance zero. However, the energy storage system (ESS) will be significantly oversized. Postcompensation reduces the power rating of the ESS but the hourly energy imbalance may not be reduced to zero when a large and long-lasting power imbalance occurs. A linear regression forecasting algorithm is developed for a two-stage precompensation algorithm to precharge or predischarge the ESS based on the predicted energy imbalance. An equivalent charge cycle estimation method is proposed to evaluate the effect of providing the energy balancing service on battery life. The performance comparison shows that the precompensation method reduces the size of the ESS by 30% with satisfactory performance.

[1]  Vilayanur V. Viswanathan,et al.  The Wide-Area Energy Storage and Management System – Battery Storage Evaluation , 2009 .

[2]  F. V. P. Robinson,et al.  Analysis of Battery Lifetime Extension in a Small-Scale Wind-Energy System Using Supercapacitors , 2013, IEEE Transactions on Energy Conversion.

[3]  Jian Ma,et al.  Integration of uncertainty information into power system operations , 2011, 2011 IEEE Power and Energy Society General Meeting.

[4]  T. Guena,et al.  How Depth of Discharge Affects the Cycle Life of Lithium-Metal-Polymer Batteries , 2006, INTELEC 06 - Twenty-Eighth International Telecommunications Energy Conference.

[5]  G. Joos,et al.  Supercapacitor Energy Storage for Wind Energy Applications , 2007, IEEE Transactions on Industry Applications.

[6]  Jian Ma,et al.  Impacts of wind generation on regulation and load following requirements in the California system , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[7]  Ning Lu,et al.  A Coordinating Algorithm for Dispatching Regulation Services Between Slow and Fast Power Regulating Resources , 2014, IEEE Transactions on Smart Grid.

[8]  Jian Ma,et al.  Impacts of integration of wind generation on regulation and load following requirements of California power systems , 2008, 2008 5th International Conference on the European Electricity Market.

[9]  Ning Lu,et al.  An optimized autoregressive forecast error generator for wind and load uncertainty study , 2011 .

[10]  Michael Milligan,et al.  Large-Scale Wind Integration Studies in the United States: Preliminary Results , 2009 .

[11]  Shuai Lu,et al.  Wide-Area Energy Storage and Management system to Balance Intermittent Resources in the Bonneville Power Administration and California ISO Control Areas , 2008 .

[12]  Mike Barnes,et al.  Power quality and stability improvement of a wind farm using STATCOM supported with hybrid battery energy storage , 2006 .

[13]  Daniel S. Kirschen,et al.  Estimating the Spinning Reserve Requirements in Systems With Significant Wind Power Generation Penetration , 2009, IEEE Transactions on Power Systems.

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

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

[16]  James F. Manwell,et al.  Lifetime Modelling of Lead Acid Batteries , 2005 .

[17]  J.A.P. Lopes,et al.  On the optimization of the daily operation of a wind-hydro power plant , 2004, IEEE Transactions on Power Systems.