Sizing Advanced Flywheel Energy Storage Clay Hearn1, Sid Pratap1, Michael Lewis1, Robert Hebner1, Dongmei Chen2, and Raul Longoria2 1 The Center for Electromechanics at The University of Texas at Austin 2 The Department of Mechanical Engineering at The University of Texas at Austin Introduction Data Driven Flywheel Sizing Analysis Energy storage needs to be designed to account for operational performance, not just peak power or To understand how location affects energy storage sizing, home and solar generation data provided by Pecan Street, Inc. was used to study energy. Consequently an improved design approach using an optimal control technique has been flywheel sizing at different levels throughout a power distribution system. Pecan Street Inc. is an active R&D effort in Austin TX to implement and developed. This method allows the loss dynamics of the flywheel system to be incorporated into the evaluate smart grid technology in a functioning community. There are 741 homes in this community, and 25% of these homes have solar sizing procedure, and allows trade studies to be performed with different flywheel sizes to minimize installations, which translates into approximately 1 MW of generation capacity. Flywheel energy storage was sized at the individual homes, local peak grid power use. The effectiveness of the sizing methodology is illustrated through a case study transformers, and community level. based on home consumption and solar generation data collected from one of the premier Smart Grid programs in the world, the Pecan Street Project in Austin, Texas. Sizing Flywheel Energy Storage Flywheels are electro-mechanical devices which kinetically store energy via a high speed rotating mass. Motor-generators are used to transfer energy to and from the spinning mass, which allows flywheels to have improved power performance over the most advanced battery systems. A key aspect of flywheel energy storage, which separates it from other devices such as batteries or ultracapacitors, is that energy transfer, to charge and discharge a flywheel, is provided by motor- generators. Therefore, energy storage capacity and power capability can be tailored to meet specific grid requirements. Designers must understand power and energy storage requirements at different locations within the utility grid, since grid design does not promote a single optimal storage approach for all locations. Load profile for individual home in Mueller Aggregate load profile for transformer (~8 Aggregate load profile for all 741 homes in the Mueller community. Power spikes due to A/C Loads. Data To properly size energy storage for a given load demand, or power generation source, a controller homes) in Mueller community. Data provided community. Natural smoothing of peak loads through provided by Pecan Street Inc. should be selected which will determine the real time grid power requirements to maintain the stored by Pecan Street Inc. aggregation. Data provided by Pecan Street Inc. energy of the storage device. For flywheel energy storage, the change in stored energy with respect Flywheel Energy Storage Sizing for Individual Homes Transformer Energy Storage vs. Peak Grid Power Community Energy Storage vs. Peak Power to time will equal the grid power into the flywheel minus the load demand and minus losses which 25 Home 1412 100 10000 may come from windage or bearings. For flywheels, the losses can be estimated by using a linear Home 1420 time constant. By selecting an optimal control law for the controller, parametric studies can be 20 Home 1421 Home 1422 1000 performed to evaluate performances of energy storage versus power output. Home 1423 10 15 Home 1424 100 Home 1425 Home 1426 1 Time Constant 200 Hrs Wind 10 Time Constant 200 Hrs 10 Home 1427 Time Constant 50 Hrs Home 1439 Time Constant 50 Hrs 5 Home 1446 0.1 1 FW Delivered Energy [kWh] Energy Delivered FW Home 1458 10.0 15.0 20.0 25.0 30.0 [kWh] Energy Delivered FW 1000 1500 2000 2500 3000 Solar [kWh] Delivered Energy Flywheel Home 1463 Peak Grid Power [kW] Total Demand: DL 0 Peak Grid Power [kW] 0% 20% 40% 60% 80% 100% Percent Decrease in Peak Grid Power Usage Trade-offs between flywheel storage capacity and peak Trade-offs between flywheel storage capacity and peak grid 푑푄푓푤 1 Trade-offs between flywheel storage capacity and reduced = 푃푔 − 퐷퐿 − 푄푓푤 grid power demand for flywheel location at local power demand for the 741 home community. Larger flywheel 푑푡 휏 peak grid power demand at the home level. Study shows a 2 푓푤 transformers. storage for diurnal applications will require flywheels with kWh / 6.2 kW flywheel could reduce peak power draw for low losses and higher spin-down time constants Grid : Pg most homes by 30 – 60%. Controller S Flywheel Energy: Qfw Location Peak Demand Diurnal Storage Power Smoothing Conclusions Power Optimal Controller Cost Function Singe Home 5.7 KW 15 kWh / 2 kWh / Flywheel energy storage specifications can be derived at various (average) 6 kW 6.2 kW locations within a system using the proposed methodology with real- The following cost function is used to develop the optimal control law. The first term of the cost Transformer 30.7 kW 70 kWh / 6 kWh / world data. For the demonstrated system, our study found: function is an end constraint which requires the flywheel stored energy at the end of the simulation (8 homes) 16.3 kW 12 kW to equal the initial amount of stored energy. The integral portion of the cost function seeks to • Diurnal storage require flywheels sized to 0.5–0.2 C-rate capabilities Community 2500 kW 5.9 MWh / 450 kWh / minimize the grid power, P , and deviation of flywheel stored energy, Q , from the initial stored • Power smoothing require flywheel designs with increase power rate g fw (741 homes) 1200 kW 300 kW energy, Q0. Parametric studies on changing the values of a and b can be performed to study capabilities of 1–3C tradeoffs between grid power requirements and energy storage sizing. • C-rate requirements decrease as the flywheel is moved to higher Publications locations in the grid 1 2 1 2 퐽 푡 = 푆 푄 푇 − 푄 + 풂 푄 푡 − 푄 + 풃푃2 푡 푑푡 1. C.S. Hearn, M.C. Lewis, S.B. Pratap, R.E. Hebner, F.M. Uriate, D. Chen, R.G. Longoria. • Reduction of flywheel losses is critical for effective use of diurnal 0 2 푞 푓푤 0 2 푓푤 0 푔 Utilization of Optimal Control Law to Size Grid-Level Flywheel Energy Storage. Submitted to IEEE Transactions on Sustainable Energy, July 9, 2012. energy storage .