Performance Simulation of Energy Storage Technologies for Renewable - - PowerPoint PPT Presentation

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): Pdemand = Pgeneration

• 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:

Edemand = Egeneration

• Energy Storage has the potential to enable high penetration
• f 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 t P t

Storage

P t P t P t

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, CO2) 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 = E0+PinTs

Ts: Time step E: energy stored after Ts E0: energy stored before Ts Pin: Power input Discharge:

E = E0-PoutTs

State of charge:

SOC = E/Emax

1 ≥ SOC ≥ 0

Ideal Energy Storage

Number of cycles Nc:

Nc = N0+PTs/2Emax

1 cycle = 1 charge and 1 discharge Efficiency Charge:

E = E0+PinTs ηc

Discharge

E = E0-PoutTs /ηd

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

Diagram of pumped hydroelectric energy storage [1]

1. Transmission 2. Transformer 3. Motor-generator 4. Lower reservoir 5. Tail race 6. Pump-turbine 7. Penstock 8. Upper reservoir 9. Local loads

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(Pin/Pout)

• 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 = E0-PoutTs ηd

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

• xidized

[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) Cr = IkTs

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 LI2

Experimental SMES composition [1]

SMES

Capacity: given by power conversion

• r 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 = 0.5Jω2

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 = 0.5CV2

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

No effects Resistive, PC High Capacitor ratings UC No effects Parasitic, friction, SD, PC High Rotational speed Flywheel No effects PC, Refrigeration, SD, PC High Coil rating SMES No effects Resistive, PC, SD, parasitic High Cell number Reflow Peukert effect Lifetime decreases Resistive, PC, SD High Cell number Batteries Emissions No effects ηd Medium Cavern volume CAES No effects ηp, ηg, self- discharge Slow Reservoir volume PHES Other Cycling Losses Pout Emax Technology

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

1.

• A. Ter-Gazarian, Energy Storage for Power Systems,

Peter Peregrinus, 1994

2.

http://www.sandia.gov/media/NewsRel/NR2001/nort

• n.htm

3.

• D. Linden, T.B. Reddy, Handbook of Batteries, 3rd

edition, McGraw-Hill, 2002

4.

http://www.electricitystorage.org/pubs/2001/IEEE_P ES_Summer2001/Miyake.pdf

5.

Handbook of Energy Storage for Transmission and Distribution Applications, EPRI - DOE, Washington D.C., 2003

QUESTIONS?

Email: silvac@u.washington.edu

Load leveling

time P Daily Load Shape

Load leveling

Load leveling

Benefits

Supply cheap off-peak power to on-peak times Keep base load units on line during off-peak

Issues

Need high price differential to be economic Round trip efficiency must be high Enables base load - CO2 release may increase Daily load shape sets storage and power

requirements

Major motivator for existing storage facilities

Investment Deferral

Idea: Optimal utilization of transmission

investment

Transfer % Above Only a few hours at maximum load

Investment Deferral

Storage allows line to operate closer

to average power output

Transfer % Above Storage

Investment Deferral

Benefits

More capacity (MWh transferred) from same

line

Can defer transmission construction Transmission losses reduced for same

energy transfer

Also provides peak shaving benefits

Issues

How does storage capture value of

investment deferral and reduced losses in deregulated market?