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112/8/2017

Energy Storage in Microgrids: Challenges, Applications, and Research Need

Hamidreza NazaripouyaUniversity of California, Los Angeles

December 8, 2017

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Introduction

Micro Grid

Source: U.S. Department of Energy

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Microgrid Challenges

Microgrid Stability

Single line diagram of the microgrid in islanding transition System instability during islanding due to power imbalance

R. Majumder, "Some Aspects of Stability in Microgrids," IEEE Transactions on Power Systems, vol. 28, no. 3, pp. 3243 - 3252, 2013.

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Microgrid Power Management

Santa Rita Jail Microgrid Single Line Diagram

Autonomous and robust power management system

Sustainable delivery of electrical power to local loads

Optimizing allocated objectives

Energy production,

Operational cost,

Energy efficiency,

Power loss

AC/DC hybrid microgrid

High Number of scenarios to capture all dynamics of the system

Grid-connected mode and islanded mode

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Microgrid Power Quality and Reliability

Single line diagram of CSIRO REIF microgridFigure 6 Current THD at different level of PV power

Figure 7 Voltage THD at different level of PV power Figure 8 Current THD of odd harmonics

A. Vinayagam, K. Swarna, S. Y. Khoo and A. Stojcevski, "Power Quality Analysis in Microgrid: An Experimental Approach," Journal of Power and Energy Engineering, vol. 4, no. 4, pp. 17-34, 2016

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Energy Storage Technologies

1. Electrical energy storage (EES): This category can be divided into:1. Magnetic/current energy storage such as Superconducting Magnetic Energy Storage

(SMES).2. Electrostatic energy storage such as capacitors and supercapacitors.

2. Mechanical energy storage (MES): This category can be also divided into:1. Kinetic energy storage such as flywheel.2. Potential energy storage such as Compressed Air Energy Storage (CAES) or Pumped

Hydroelectric Storage (PHS).3. Chemical energy storage (CES): This class of energy storage includes

1. Electrochemical energy storage such as conventional batteries (lead-acid, lithium ion) and flow-cell batteries (vanadium redox).

2. Chemical energy storage such as fuel cells. 3. Thermochemical energy storage such as solar hydrogen, solar metal.

4. Thermal energy storage: This type of technology also includes 1. Low temperature energy storage such as cold aquifer thermal energy storage, and

cryogenic energy storage.2. High temperature energy storage such as steam or hot water accumulators, graphite, hot

rocks and concrete, and latent heat systems.

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Energy Storage TechnologiesEnergy Storage

TechnologyRating Characteristic Dynamics Space Requirements Performa

nceExample of Applications

Discharge/charge rate (MW)

Discharge duration Response time Ramp rate Energy Density (Wh/kg)

Power Density(W/kg)

Efficiency(%)

Battery Energy Storage 0-40 Minutes–hours Milliseconds MW/sec 10-250 70-300 70–90

• Energy management

• Grid stabilization

• Power quality

•Renewable source integration

Capacitors (Super Capacitors)

0.01–0.05(0.01–0.3)

Milliseconds–1 hour

Milliseconds MW/sec0.05–5

(0.1–15)

100, 000(500–

10,000)

60–90(75–95)

• Power quality

• Frequency regulation

Flywheel Energy Storage (FES)

0.002–0.25Milliseconds –15

minInstantaneous MW/min 5–130 400–1500 90–95

• Load leveling


• Peak shaving and off peak storage

• Transient stability

Fuel Cell 0.001–50 Sec–24+ hour Milliseconds MW/min 800–10,000 500+ 20-90

Compressed Air Energy Storage (CAES)

0.1-300 1-24+ hour Minutes MW/min 30–60 - 42–89


• Backup and seasonal reserves

• Renewable integration

Superconducting Magnetic Energy Storage (SMES)

0.1–10Milliseconds–10

secInstantaneous MW/milisec 0.5–5 500–2000 > 97

• Power quality


Pumped Hydroelectric Storage (PHS)

0.1–5000 1-24+ hourSeconds -Minutes

MW/sec 0.5–1.5 - 71–85


• Backup and seasonal reserves

• Regulation service

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Energy Storage Applications in Microgrids

Stability Enhancement

Voltage Stability Line resistance to line reactance ratio (R/X)

The impact of active and reactive power

Battery energy storage and grid-tie inverter, source of active and reactive power

Frequency Stability Inverter interfaced distributed generation, low or lack of inertia

Energy storage system to act as virtual inertia

Battery energy storage systems, supercapacitors, SMES, and FES aresuitable

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Energy Management

Reducing the complexity of energy management problem Smoothing the renewable energy power generation Managing the demand side balancing demand and supply

Time-shifting of generation and demand Shift the power demand over time Shift the power generation

Reducing Power loss, and improving efficiency Maximize the local energy utilization and reduce the transferred

power from the main grid. Energy loss is proportional to the square of the current (𝑅𝑅𝐼𝐼2)

Reactive power compensation

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Power Quality Improvement

Renewable Intermittency Compensation

Voltage Support

Power Factor Correction

Phase balancing

Harmonic Compensation

Figure 9 solar power smoothing by energy storage in Borrego Springs microgrid [1]

Figure 10 reactive power control for power factor correction [2]

[1] N. Bartek, "Borrego Springs Microgrid Demonstration Overview," Society of American Military Engineers, San Diego, CA,2015.[2] T. Bialek, "borrego springs microgrid demonstration project," San Diego Gas & Electric, San Diego, CA, 2013.

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Reliability Improvement

Ride-through and Bridging

Resource Adequacy

Figure 11 single line diagram of the Glacier’s power circuit

Resiliency Improvement

Ability of the system to withstand and recover from disruptive events

Minimize the duration, intensity, and the negative impacts of unfavorable events

Virtual inertia increases the tolerance and robustness of the entire system to sudden changes

Enhances the flexibility of microgrid for immediate reconfiguration

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Challenges and Barriers:

Cost Cost of technology Cost of supplementary equipment Cost of installation, integration, and commissioning

Type, Size, and Placement Standards to evaluate and compare the quality and performance A transparent guideline

Rules, Regulation, and Standards Encourage stakeholders to invest Clear and accurate vision about the performance

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Setup a Testbed Platform

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Problem Description

Solar Power Intermittency

1) Ramp-rate control:

Only smooths steep change in PV power by ramping up or down (2%/min)

Requires SOC control to avoid battery discharge over time

[5] I. de la Parra, J. Marcos, M. García and L. Marroyo, "Dynamic ramp-rate control to smooth short-term power fluctuations in large photovoltaic plants using battery storage systems," IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Florence, 2016, pp. 3052-3057.

[4] Marcos, Javier, et al. "Control strategies to smooth short-term power fluctuations in large photovoltaic plants using battery storage systems." Energies 7.10 (2014): 6593-6619.

0 200 400 600 800 1000 1200 1400

Time (Min)

-5

0

5

10

15

20

25

Pow

er (k

W)

Solar Power

Solar PowerMoving Average

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Problem Description


2) Moving Average:

No need for any type of SOC control to prevent the continuous battery discharge

Requires considerable amount of storage capacity

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Problem Description


Objectives:

Generate a reference in real time to be followed by storage

Avoid SOC control

Minimize the use of battery capacity

0 1000 2000 3000 4000 5000 6000 7000

Time (Min)

-10

0

10

20

30

Pow

er (k

W)

Solar Power

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Research Solution

Smoothing Solar Power Intermittency

Time domain Frequency domain

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Research Solution


Time domain Frequency domain

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Problem Description


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Research Solution


FIR filters vs. IIR filters

Controller design parameters: Passband edge Stopband edge Passband ripple Stopband ripple Filter order

Group delay vs. phase angle

Controller characteristic and features High damping performance maximum attenuation at the stopband Minimum use of battery capacity minimum-group-delay in the passband Minimum cost and complexity of the controller minimum length

minℎ 𝑛𝑛

𝛼𝛼1𝑁𝑁 + 𝛼𝛼2𝜀𝜀1 + 𝛼𝛼3𝜀𝜀2

s.t. 𝐻𝐻(𝑒𝑒𝑗𝑗𝑗𝑗) 2 ≤ 𝜀𝜀1 𝑓𝑓𝑓𝑓𝑓𝑓 𝜔𝜔 ≥ 𝜔𝜔𝑐𝑐 (1)

grd 𝐻𝐻 𝑒𝑒𝑗𝑗𝑗𝑗 ≤ 𝜀𝜀2 𝑓𝑓𝑓𝑓𝑓𝑓 𝜔𝜔 ≤ 𝜔𝜔𝑐𝑐

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Research Solution


𝐻𝐻 𝑒𝑒𝑗𝑗𝑗𝑗 = �𝑛𝑛=0

𝑁𝑁−1

ℎ 𝑛𝑛 𝑒𝑒−𝑗𝑗𝑗𝑗𝑛𝑛 A length-N FIR filter:

Group delay: 𝜏𝜏 𝜔𝜔 = grd 𝐻𝐻 𝑒𝑒𝑗𝑗𝑗𝑗 = −𝑑𝑑𝑑𝑑𝜔𝜔

arg 𝐻𝐻 𝑒𝑒𝑗𝑗𝑗𝑗 = −𝑑𝑑𝑑𝑑 𝜔𝜔𝑑𝑑𝜔𝜔

𝜏𝜏 𝜔𝜔 = 𝑅𝑅𝑒𝑒𝑅𝑅𝑅𝑅𝐹𝐹.𝑇𝑇. 𝑛𝑛ℎ 𝑛𝑛𝐹𝐹.𝑇𝑇. ℎ 𝑛𝑛

𝜏𝜏 0 = �𝑅𝑅𝑒𝑒𝑅𝑅𝑅𝑅𝐹𝐹.𝑇𝑇. 𝑛𝑛ℎ 𝑛𝑛𝐹𝐹.𝑇𝑇. ℎ 𝑛𝑛

𝑗𝑗=0

= 0 ⇔ �𝑛𝑛=0

𝑁𝑁−1

𝑛𝑛ℎ 𝑛𝑛 = 0

𝑃𝑃 = 𝑧𝑧𝑛𝑛 + 𝑅𝑅1𝑧𝑧𝑛𝑛−1 + ⋯+ 𝑅𝑅𝑛𝑛−1𝑧𝑧 + 𝑅𝑅𝑛𝑛 � 𝐵𝐵0 + �𝑗𝑗=1

𝑛𝑛

𝐵𝐵𝑗𝑗𝑅𝑅𝑗𝑗 = 0, 𝑅𝑅𝑖𝑖 ∈ ℝ For minimum-phase:

𝜌𝜌∗ ≔ inf𝑝𝑝∈𝑃𝑃

𝜌𝜌 𝑝𝑝

𝜌𝜌 𝑝𝑝 = 𝑚𝑚𝑅𝑅𝑚𝑚 𝑧𝑧 𝑝𝑝 𝑧𝑧 = 0, 𝑧𝑧 ∈ ℂ𝑝𝑝∗ 𝑧𝑧 = 𝑧𝑧 − 𝛾𝛾 𝑛𝑛−𝑘𝑘 𝑧𝑧 + 𝛾𝛾 𝑘𝑘 ∈ 𝑃𝑃

The length of an FIR filter (N) is a quasi-convex function of its coefficients (𝑚𝑚)

Designing the minimum-phase, low group delay FIR filter can be expressed as the second-order cone programming (SOCP)

12/8/2017 24

Research Solution


Optimization Problem Optimization Variable: ℎ[𝑛𝑛]

minℎ[𝑛𝑛]

�� ℎ[𝑛𝑛]𝑒𝑒−𝑗𝑗𝜔𝜔𝑛𝑛𝑁𝑁−1

𝑛𝑛=0

� + 𝛼𝛼 �� ℎ[𝑛𝑛]𝑁𝑁−1

𝑛𝑛=1

� 𝜔𝜔𝑐𝑐 ≤ 𝜔𝜔 ≤ 𝜋𝜋

subject to:

�ℎ[𝑛𝑛] = 1𝑁𝑁−1

𝑛𝑛=0

𝑅𝑅𝑛𝑛𝑑𝑑 �𝑛𝑛ℎ[𝑛𝑛] = 0𝑁𝑁−1

𝑛𝑛=0

Optimization Problem 2 minimize N subject to

‖𝐴𝐴𝑘𝑘𝑚𝑚(𝑁𝑁)‖2 ≤ 0.7079 (−3 𝑑𝑑𝐵𝐵) 𝑘𝑘 ∈ 𝐼𝐼 , where 𝐼𝐼 = {𝑘𝑘|𝜔𝜔𝑘𝑘 ≥ 𝜔𝜔𝑐𝑐}

guarantees the minimum-phase filter

Makes the group delay at 𝜔𝜔 = 0, zero, minimum delay in low frequency

Maximizes the attenuation at the stopband

Makes the magnitude at 𝜔𝜔 = 0 needs equal to one

Minimizes the filter order while satisfies the cut-off frequency

12/8/2017 25

Research Solution

Time (min)0 500 1000 1500

Pow

er (k

W)

-5

0

5

10

15

20

25SolarHerrmann and Schuessler Filter OutputProposed Optimized Filter OutputMoving Average Filter Output

Frequency (mHz)0 0.01 0.02 0.03 0.04 0.05 0.06

Gro

up d

elay

(in

sam

ples

)

0

50

100

150

200

250

Proposed ApproachMoving Average ApproachHermann and Schuessler Approach

Real Part0.82 0.84 0.86 0.88 0.9 0.92

Imag

inar

y Pa

rt

0.47

0.48

0.49

0.5

0.51


45 percent reduction in the BESS capacity compared with the common moving average technique

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Research Solution

Smoothing Solar Power Intermittency in Real-time

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Research Solution

Real Time Level 3 Charger Power Demand Cut

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Problem Description

Voltage Regulation

Common practice to regulate the voltage are less effective On-Load Tap Changer (OLTC) transformer, Step Voltage Regulator (SVR), Switch capacitor banks

The system operators still curtail the renewables generation

The centralized control scheme requires communication channels significant investment in data sensing and collection infrastructure

In distribution system reactive power compensation is less effective relatively high ratio of the R/X.

Source: http://sine.ni.com/cs/app/doc/p/id/cs-15810#prettyPhoto

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Research Solution

Voltage RegulationVVIZIZIIZV busbusbus ∆+=∆+=∆+=

000 )(

×

=

∆

∆

∆

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V

V

V

−−−−

×

=

∆

∆

∆

∆∆

0

211

12

11

12

11

1

21

21

21

2222221

1111211

2

1

i

m

mm

ii

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V

V

V

VV

IiZZZZI

ZZZZI

ZZZZVV iiiimmiimiiii )()()(

11

11

11

112

11

1212

0 −+−++−+=

×

−−−

−−−

−−−

=

−

−

−

mmmmmmmmm

mm

mm

imii

m

iii

iii

I

II

ZZZZ

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)()()(

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)(

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( )ieqk

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eq

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ki

ki

kj

ki

+

+×

−+−=

0

0max0

min

)()(

( )ieqk

ieqk

eq

ieqkkj

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)()(

Best Location for BESS Bus number 9 Minimum Active power (BESS) 0.1196 pu Minimum Reactive Power (GTI) 0.3566 pu

Bus #

Voltage profile

P=0pu P=0.2pu P=0.4pu P=0.6p

u P=0.8p

u P=1p.

u 1 1.0600 1.0600 1.0600 1.0600 1.0600 1.0600 2 1.0450 1.0450 1.0450 1.0450 1.0450 1.0450 3 0.9978 0.9989 0.9998 1.0007 1.0014 1.0017 4 0.9982 1.0003 1.0023 1.0041 1.0057 1.0065 5 1.0032 1.0065 1.0096 1.0127 1.0154 1.0173 6 1.0362 1.0380 1.0396 1.0413 1.0424 1.0420 7 1.0157 1.0158 1.0159 1.0160 1.0156 1.0134 8 1.0450 1.0452 1.0453 1.0454 1.0449 1.0428 9 0.9964 0.9955 0.9946 0.9937 0.9922 0.9884

10 0.9955 0.9950 0.9946 0.9941 0.9930 0.9898 11 1.0118 1.0124 1.0130 1.0135 1.0135 1.0117 12 1.0202 1.0218 1.0233 1.0248 1.0258 1.0252 13 1.0143 1.0157 1.0170 1.0183 1.0190 1.0181 14 0.9879 0.9880 0.9880 0.9881 0.9875 0.9849

Best Location for BESS Bus number 7

Minimum Active power (BESS) 0.0845 pu Minimum Reactive Power (GTI) 0.4338 pu

Best Location for BESS

Bus number 9

Minimum Active power (BESS)

0.1196 pu

Minimum Reactive Power (GTI)

0.3566 pu

Bus #

Voltage profile

P=0pu

P=0.2pu

P=0.4pu

P=0.6pu

P=0.8pu

P=1p.u

1

1.0600

1.0600

1.0600

1.0600

1.0600

1.0600

2

1.0450

1.0450

1.0450

1.0450

1.0450

1.0450

3

0.9978

0.9989

0.9998

1.0007

1.0014

1.0017

4

0.9982

1.0003

1.0023

1.0041

1.0057

1.0065

5

1.0032

1.0065

1.0096

1.0127

1.0154

1.0173

6

1.0362

1.0380

1.0396

1.0413

1.0424

1.0420

7

1.0157

1.0158

1.0159

1.0160

1.0156

1.0134

8

1.0450

1.0452

1.0453

1.0454

1.0449

1.0428

9

0.9964

0.9955

0.9946

0.9937

0.9922

0.9884

10

0.9955

0.9950

0.9946

0.9941

0.9930

0.9898

11

1.0118

1.0124

1.0130

1.0135

1.0135

1.0117

12

1.0202

1.0218

1.0233

1.0248

1.0258

1.0252

13

1.0143

1.0157

1.0170

1.0183

1.0190

1.0181

14

0.9879

0.9880

0.9880

0.9881

0.9875

0.9849

Best Location for BESS

Bus number 7

Minimum Active power (BESS)

0.0845 pu

Minimum Reactive Power (GTI)

0.4338 pu

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Research Solution

Voltage Regulation

Model-Free Optimal Control of Battery Energy Storage

0 200 400 600 800 1000 1200 1400Time (min)

-1

0

1

2

3

Pow

er (k

W)

Solar Power

0 200 400 600 800 1000 1200 1400Time (min)

0

1

2

3

Pow

er (k

W)

Consumer Load Profiles

Load 1 Load 2 Load 3

0 200 400 600 800 1000 1200 1400Time (min)

0.99

1

1.01

1.02

Vol

tage

(pu)

Voltage Profile (Battery Node)Battery: offBattery: on

0 200 400 600 800 1000 1200 1400Time (min)

0

2

4

6

Pow

er (k

VA

)

Inverter Apparent Power

Active and Reactive ControlReactive ControlX: 610.5

Y: 6.392

X: 1264

Y: 2.5

12/8/2017 31

Research Solution

Voltage Regulation

Model-Free Optimal Control of Battery Energy Storage

0.0

0.2

0.4

0.6

0.8

1.0

1 41 81 121 161 201 241 281 321 361 401

Power Factor

212

212

213

213

214

1 41 81 121 161 201 241 281 321 361 401

AC Voltage (V)

10000

15000

20000

25000

30000

1 41 81 121 161 201 241 281 321 361 401

Apparent Power (VA)

20 percent reduction in the BESS capacity compared with reactive power control

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Current ActivitiesRun Pilot Projects: City of Santa Monica

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Current ActivitiesRun Pilot Projects: UCLA Parking Structure # 9

12/8/2017 34

PublicationsPatent R. Gadh, H. Nazaripouya, P. Chu, “Battery Energy Storage Control System”, UCLA Case No. 2016-213, Oct 2, 2015

Book Chapters H. Nazaripouya, "Energy Storage at Different Grid Levels - Technology, Integration, and Market Aspects: Chapter 5-Energy Storage in

Microgrids," Submitted to IET. Y. Wang, H. Nazaripouya, and Rajit Gadh "Classical and Recent Aspects of Power System Optimization: Chapter 13: Smart Grid

Optimizations: System Scalability and Uncertainties," Under Preparation for Elsevier.

Journals and Transactions: B. Wang, Y. Wang, H. Nazaripouya, C. Qiu, C. C. Chu and R. Gadh, "Predictive Scheduling Framework for Electric Vehicles with

Uncertainties of User Behaviors," in IEEE Internet of Things Journal, vol. 4, no. 1, pp. 52-63, Feb. 2017. H. Nazaripouya and S. Mehraeen, "Modeling and Nonlinear Optimal Control of Weak/Islanded Grids Using FACTS Device in a Game

Theoretic Approach," in IEEE Transactions on Control Systems Technology, vol. 24, no. 1, pp. 158-171, Jan. 2016. H. Nazaripouya, C. Chu, H. R. Pota, and R. Gadh, "Battery Energy Storage System Control for Intermittency Smoothing Using Optimized

Two-Stage Filter", IEEE Transactions on Sustainable Energy, Submitted for publication, under review 2017. M. Majidpour, H. Nazaripouya, C. Chu, H. R. Pota, and R. Gadh, "Fast Univariate Time Series Prediction of Solar Power for Real-Time

Control of Energy Storage System at UCLA Campus", Sustainable Energy Technologies and Assessments, Submitted for publication, underreview 2017.

H. Nazaripouya, C. Chu, H. R. Pota, and R. Gadh, "Design of Minimum-length, Minimum-phase, Low-group-delay FIR Filter Using Convex

Optimization Method" IEEE Transactions on Signal Processing, Submitted for publication.

H. Nazaripouya, C. Chu, H. R. Pota, and R. Gadh, "Model-Free Optimal Control of Battery Energy Storage for Voltage Regulation inDistribution Systems" Under Preparation for IEEE Transactions on Power Systems.

A. Ghasem Azar, H. Nazaripouya, B. Khaki, C.Chu, R. Gadh, and R. H. Jacobsen "A Non-Cooperative Framework for Coordinating aNeighborhood of Distributed Prosumers" Under Preparation for IEEE Transactions on Smart Grid.

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PublicationsConferences H. Nazaripouya, B. Wang, Y. Wang, P. Chu, H. R. Pota and R. Gadh, "Univariate time series prediction of solar power using a hybrid

wavelet-ARMA-NARX prediction method," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX,2016, pp. 1-5.

Y. Wang, B. Wang, Tianyang Zhang, H. Nazaripouya, C. C. Chu and R. Gadh, "Optimal energy management for Microgrid with stationaryand mobile storages," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D), Dallas, TX, 2016, pp. 1-5.

Bin Wang, Rui Huang, Yubo Wang, H. Nazaripouya, Charlie Qiu, Chi-Cheng Chu, Rajit Gadh, "Predictive scheduling for Electric Vehiclesconsidering uncertainty of load and user behaviors," 2016 IEEE/PES Transmission and Distribution Conference and Exposition (T&D),Dallas, TX, 2016, pp. 1-5.

H. Nazaripouya, H. Mokhtari and E. Amiri, "Using optimal controller to parallel three-phase 4-leg inverters with unbalance loads," 2016IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), Delhi, 2016, pp. 1-6.

H. Nazaripouya, Y. Wang, P. Chu, H. R. Pota and R. Gadh, "Optimal sizing and placement of battery energy storage in distribution systembased on solar size for voltage regulation," 2015 IEEE Power & Energy Society General Meeting, Denver, CO, 2015, pp. 1-5.

Y. Wang, H. Nazaripouya, C. C. Chu, R. Gadh and H. R. Pota, "Vehicle-to-grid automatic load sharing with driver preference in micro-grids," IEEE PES Innovative Smart Grid Technologies, Europe, Istanbul, 2014, pp. 1-6.

H. Nazaripouya and S. Mehraeen, "Optimal PMU placement for fault observability in distributed power system by using simultaneousvoltage and current measurements," 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, 2013, pp. 1-6.

S. Lamichhane, H. Nazaripouya and S. Mehraeen, "Micro Grid Stability Improvements by Employing Storage," 2013 IEEE GreenTechnologies Conference (GreenTech), Denver, CO, 2013, pp. 250-258.

H. Nazaripouya and S. Mehraeen, "Control of UPFC using Hamilton-Jacobi-Bellman formulation based neural network," 2012 IEEE Powerand Energy Society General Meeting, San Diego, CA, 2012, pp. 1-8.

H. Nazaripouya and H. Mokhtari, "Control of parallel three- phase inverters using optimal control and SVPWM technique," 2009 IEEEInternational Symposium on Industrial Electronics, Seoul, 2009, pp. 1823-1828.

H. Nazaripouya and H. Mokhtari, "Improved optimal control technique for control of parallel three- phase inverters," 2009 InternationalConference on Electric Power and Energy Conversion Systems, (EPECS), Sharjah, 2009, pp. 1-6.

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Energy Storage in Microgrids: Challenges, Applications, and Research Need��Hamidreza Nazaripouya��University of California, Los Angeles��Slide Number 2Slide Number 3Slide Number 4Slide Number 5Slide Number 6Slide Number 7Slide Number 8Slide Number 9Slide Number 10Slide Number 11Slide Number 12Slide Number 13Slide Number 14Slide Number 15Slide Number 16Slide Number 17Slide Number 18Slide Number 19Slide Number 20Slide Number 21Slide Number 22Slide Number 23Slide Number 24Slide Number 25Slide Number 26Slide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34Slide Number 35Slide Number 36

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