Performance Simulation of Energy Storage Technologies for Renewable Energy Integration Cesar A. Silva Monroy Ph.D. Student – Electrical Engineering University of Washington Energy Seminar – October 8, 2009
Overview � Introduction � Power System Applications � Modeling � Pumped Hydro Energy Storage � Compressed Air Energy Storage (CAES) � Batteries � Superconducting Magnetic Energy Storage (SMES) � Flywheels � Ultracapacitors � Conclusions � References
Introduction Renewable energy resources such as wind and solar are � stochastic in nature Current power systems must keep the power balance � between generation and demand (+ losses): P demand = P generation Power imbalance between demand and generation is � aggravated by stochastic resources Energy storage can change the way we operate power � systems Future power system will need to keep energy balance: � E demand = E generation Energy Storage has the potential to enable high penetration � of renewable energy resources
Power System Applications � Load leveling � Investment deferral � Active and reactive power flow control � Emergency power supply � Focus is wind and solar integration: � Generation shaping
Generation Shaping � Wind energy is random, intermittent, over large scales and short times (10 minutes) � Load is slowly varying over 10 minutes � Wind variation must be met by change in controllable output � Generation kept on line and off market to provide response to wind costs money and emissions
Generation Shaping � Storage a solution P P t t P P t P t t Storage
Generation Shaping � Benefits � Smooth, controllable wind farm output � Reduces wind farm transfer requirement � Issues � Adds to wind farm costs, and thus cost of wind power � Regulation currently estimated to add 10% to cost of wind – not enough to pay for storage
Modeling � Generic model � Employed for optimization of power system operation � Time frame: minutes – years � No transient behavior � Capture minute to minute variations � State variables
Modeling � Parameters � Energy Capacity � Power input and output capacities � Efficiencies: Charge, Discharge, Self- discharge � Life cycling characteristic � Minimum charge � Other parameters particular to each technology (Resistance, Mass, etc.)
Modeling � Input variables: � Power input � Power output � Time step � Output variables: � State of charge � Emissions (NOx, SOx, CO 2 ) � Number of cycles
Ideal Energy Storage � Template for developing specific models � 100% efficient � Infinite charge/discharge capabilities � High energy density (energy/volume ratio) � Infinite life time � Zero emissions
Ideal Energy Storage � Charge: E = E 0 +P in T s � T s : Time step � E : energy stored after T s � E 0 : energy stored before T s � P in : Power input � Discharge: E = E 0 -P out T s � State of charge: SOC = E/E max � 1 ≥ SOC ≥ 0
Ideal Energy Storage � Number of cycles N c : N c = N 0 +PT s /2 E max � 1 cycle = 1 charge and 1 discharge � Efficiency � Charge: E = E 0 +P in T s η c � Discharge E = E 0 -P out T s / η d
Pumped Hydro Energy Storage � Hydraulic potential energy E = mgh � Charging: Pump water to a higher level reservoir � Discharging: Use stored water to run turbines connected to electric generators 1. Transmission 2. Transformer 3. Motor-generator 4. Lower reservoir 5. Tail race 6. Pump-turbine 7. Penstock 8. Upper reservoir 9. Local loads � Diagram of pumped hydroelectric energy storage [1]
Pumped Hydro Energy Storage � Capacity: given by volume � Response times are from 1 to 10 min to go from full load to full generation � Pumping efficiency is modeled as charge efficiency � Generating efficiency is modeled as discharge efficiency � Water evaporation is modeled as the self- discharge rate (very low) � No cycling effects � No emissions
CAES – Concept Stores energy in the form of a compressed gas: � E = PV ln( P in / P out ) Charging: Air is compressed in natural or artificial underground � caverns Discharging: Compressed air is released to in the combustion � process of a natural gas turbine (diabatic storage) CAES reduces overall fuel consumption � CAES concept plant (Norton mine) [2] �
CAES – Characteristics � Capacity: limited by size and conditions of storage cavern (up to thousands of MWh) � High power output ramp rate (30% of maximum load per minute) � Compression process is complex to model � About 0.75 MWh of energy are needed to store enough air for 1 MWh of energy released : � Lossless charge process � Discharge process: E = E 0 -P out T s η d � No cycling effects � There are emissions associated with generation
Batteries � Chemical potential energy � Discharge: electrons flow from anode to cathode, anode material is oxidized, cathode material is reduced � Charge: Current flow is reversed, anode material is reduced, cathode material is oxidized [3]
Batteries � Assumptions: � Current is distributed evenly through all cells in stack � All cells have the same SOC at all times � All cells have the same capacity � Capacity: given by amount of cells in series and parallel � Fast power response, in the range of seconds � Power converters efficiency are around 90% � Self-discharge
Batteries � Life cycling depends on type of battery: � Lead-acid � Sodium-Sulfur � Vanadium redox (Reflow) � Losses depend on voltage and current � Equivalent circuit:
Batteries � Lead acid: � OCV = 2.1 V � Internal resistance increases with number of cells in series, decreases with number of cycles � Voltage decreases linearly � Capacity decreases exponentially with number of cycles � Energy available decreases with higher output currents (Peukert number k ) C r = I k T s � k = 1.1-1.3
Batteries � Sodium-Sulfur � OCV = 2.08 V � Internal resistance increases with number of cells in series, decreases with number of cycles � Voltage is constant up to DOD of 60-75% � Voltage drops linearly for DOD > 60-75% � Capacity decreases linearly with number of cycles � Peukert effect
Batteries � Vanadium redox (Reflow) [4]
Batteries � Energy capacity is limited by reactant tank volumes � Power capabilities are limited by number of cells � Auxiliary equipment losses � OCV = 1.4 V � Output voltage: � V = OCV +2RT/F ln(SOC/(1-SOC)) � No Peukert effect � No cycling effect
SMES � Stores energy in the magnetic field formed by a dc current circulating in a superconducting magnetic ring E = 0.5 LI 2 � Experimental SMES composition [1]
SMES � Capacity: given by power conversion or coil ratings � Very high power capabilities � Losses: � Power conversion � Refrigeration losses: assumed constant � Self-discharge values are high if pumps are kept on
Flywheels � Rotational kinetic energy: E = 0.5 J ω 2 � Charge: motor accelerates spinning mass (rotor) � Discharge: use inertia of rotating mass to drive generator � Power conversion system needed � Cross-section of a flywheel [5]
Flywheels � Capacity: given by maximum rotational speed � Very high power charge/discharge capabilities � Losses: � Power conversion system � Bearings friction losses can be calculated as function of friction moment � Operation of magnetic bearings or low viscosity fluids cause parasitic losses � No cycling effects � No emissions
Ultracapacitor � Electric potential energy: E = 0.5 CV 2 � Charge/discharge: constant current, voltage or power � Uses double layer effect [5]
Ultracapacitor � Model as a capacitor with a series resistance � Energy capacity is increased by adding capacitors in series and parallel � Very high power capabilities � Additional losses due to power conversion � No cycling effects � Very low self-discharge
Summary Technology Emax Pout Losses Cycling Other Reservoir Slow η p, η g, self- No effects PHES volume discharge Cavern Medium η d No effects Emissions CAES volume Cell number High Resistive, PC, SD Lifetime Peukert Batteries decreases effect Cell number High Resistive, PC, No effects Reflow SD, parasitic Coil rating High PC, Refrigeration, No effects SMES SD, PC Rotational High Parasitic, friction, No effects Flywheel speed SD, PC Capacitor High Resistive, PC No effects UC ratings
Conclusions � Simulation of energy storage technologies can be carried out with a set of defined parameters � Pump-hydro, CAES and Batteries are large- scale storage � Future work � Include cost models � Optimal operation � Optimal location � Optimal size
References A. Ter-Gazarian, Energy Storage for Power Systems , 1. Peter Peregrinus, 1994 http://www.sandia.gov/media/NewsRel/NR2001/nort 2. on.htm D. Linden, T.B. Reddy, Handbook of Batteries, 3rd 3. edition , McGraw-Hill, 2002 http://www.electricitystorage.org/pubs/2001/IEEE_P 4. ES_Summer2001/Miyake.pdf H andbook of Energy Storage for Transmission and 5. Distribution Applications , EPRI - DOE, Washington D.C., 2003
QUESTIONS? � Email: silvac@u.washington.edu
Load leveling P time Daily Load Shape
Load leveling
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