A Graphical Performance-Based Energy Storage Capacity Sizing Method for High Solar Penetration Residential Feeders

This paper presents a graphical, performance-based energy storage capacity sizing method for residential feeders with high solar penetration levels. The rated power and storage capacity of an energy storage device (ESD) are calculated to fulfill a specified operational requirement. Three locations for installing ESDs are investigated: 1) consumer-owned ESDs inside single-family households; 2) utility-owned distribution transformer-level ESDs; and 3) third-party owned ESDs in a community. First, historical solar radiation data, residential household load data, and residential load models are used for creating the net load (load minus solar generation) ensembles at the house level with resolution of 15 min. Then, a novel graphical capacity selection method using equal probability lines on compressed, composite cumulative distribution function curves is developed for sizing the energy storage needs at the house, distribution transformer, and community levels. Demand-side management methods are investigated for further reducing the need of energy storage. Simulation results demonstrate that the proposed method avoids over- or under-sizing ESDs and allows the users to compare the marginal benefit of increasing the capacity of the ESD.

[1]  Yu-Hsiu Lin,et al.  Development of an Improved Time–Frequency Analysis-Based Nonintrusive Load Monitor for Load Demand Identification , 2014, IEEE Transactions on Instrumentation and Measurement.

[2]  J. Keller,et al.  High Penetration Photovoltaic Case Study Report , 2013 .

[3]  Youngwook Kim,et al.  Home appliance load disaggregation using cepstrum-smoothing-based method , 2015, IEEE Transactions on Consumer Electronics.

[4]  J.P. Barton,et al.  Energy storage and its use with intermittent renewable energy , 2004, IEEE Transactions on Energy Conversion.

[5]  Paul Denholm,et al.  Role of Energy Storage with Renewable Electricity Generation , 2010 .

[6]  Yuri V. Makarov,et al.  THE WIDE-AREA ENERGY STORAGE AND MANAGEMENT SYSTEM PHASE II Final Report - Flywheel Field Tests , 2010 .

[7]  M. Woodhouse,et al.  Residential, Commercial, and Utility-Scale Photovoltaic (PV) System Prices in the United States: Current Drivers and Cost-Reduction Opportunities , 2012 .

[8]  Wei Zhou,et al.  A novel optimization sizing model for hybrid solar-wind power generation system , 2007 .

[9]  D. A. Halamay,et al.  Optimal Energy Storage Sizing and Control for Wind Power Applications , 2011, IEEE Transactions on Sustainable Energy.

[10]  Pengwei Du,et al.  Smart meter data analysis , 2012, PES T&D 2012.

[11]  Nicholas W. Miller,et al.  Impact of High Solar Penetration in the Western Interconnection , 2010 .

[12]  Ziyad M. Salameh,et al.  Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system , 1996 .

[13]  Susan M. Schoenung,et al.  Long- vs. short-term energy storage technologies analysis : a life-cycle cost study : a study for the DOE energy storage systems program. , 2003 .

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

[15]  Ning Lu,et al.  An Evaluation of the HVAC Load Potential for Providing Load Balancing Service , 2012, IEEE Transactions on Smart Grid.