applying battery energy storage-171

7/28/2019 Applying Battery Energy Storage-171

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Applying Battery Energy Storage to Enhance the

Benefits of PhotovoltaicsFeng Cheng, Steve Willard, Jonathan Hawkins, Member, IEEE, Brian Arellano, Olga Lavrova, Member, IEEE,

Andrea Mammoli

AbstractRenewable resources are becoming more and moreobtainable and affordable due to the development of technologyand the enactment of government policies. Electric utilities arerequired to deliver reliable power to customers and must operateutility grids within strict voltage limits. As renewable energybecomes a larger player amongst the resources supplying energyto these grids, issues begin arising due to the intermittentnature of these resources. The output from solar power alignsreasonably well daytime consumption on the electricity grid,reducing the need for new coal power stations. However, highpenetration photovoltaic (PV), can lead to voltage instabilitydue to intermittencies related to cloud cover. If the PV power

is injected into a power system directly on a large scale, itmay produce issues related to dispatchability, reliability andstability. It is desirable to select a smoothing storage algorithmthat would filter out the highest rate transitions, but would stillbe fast enough to avoid significant lag with respect to currentpower production. For traditional testing, a moving averagealgorithm was used. For comparison, the author has tested twoother algorithms. The results are being compared, showing thatthe dual moving average smoothing algorithm has merits inimproving smoothness.

Index TermsBattery storage system,peak smoothing, Renew-able Energy, Photovoltaics, Smart Grid, power , energy.

I. INTRODUCTION

VARIOUS storage solutions are becoming a much neededcomponent in recent Smart Grid demonstration projects.

Public Service Company of New Mexico (PNM), in collab-

oration with other partners, is spearheading a Smart Grid

Demonstration project that will couple an advanced lead acid

battery with the output of a 500 kW substation-cited PV instal-

lation [1]. The main objectives of this demonstration project

are two-fold: (1) simultaneous smoothing of the Photovoltaic

plant output by fast-response counter-action from the battery

(2) demonstration of power peak shifting from the typical mid-

day peak by planned (slow) action from the battery [2].

Within this paper, we focus on the first objective: smoothing.

During smoothing, a battery is charged or discharged in

order to compensate for the intermittencies of the PV output.The battery makes the output smooth through fast charging

and discharging to the feeder every second. When the battery

is connected with a PV power in parallel, the resulting power

penetrating into the grid is controlled. Many similar energy

This work was supported in part by the following grants: EPRI P.A.EP P32412/C15054 and DOE -PNM DE-OE000230.

O. Lavrova and F. Cheng are with Department of Electrical Engineering,University of New Mexico, USA (e-mail: [email protected]).

A. Mammoli is with Department of Mechanical Engineering, University ofNew Mexico, USA.

S. Willard, J. Hawkinsand, and B. Arellano are with the Public UtilityService Company of New Mexico (PNM), Albuquerque, NM, 87106 USA

Fig. 1. Energy storage system

storage systems are proposed on this subject [3], [4]. However,

how to set the parameters of smoothing is not covered.In this

paper, we focus on the relationship between the parameters

of smoothing and smoothing outputs. There are a lot of

parameters influencing the results of smoothing. The capacity,

the charging and discharging rates and the ramping rate of the

battery power output are needed to be studied in order to have

an optimization of smoothing.

The moving average algorithm is designed to calculate the

rate of charging and discharging based on the real time PV

output. For the alternative methods, the author has tested twoother algorithms: dual moving average algorithm and moving

median algorithm. A dual sliding window algorithm is found

to further improve the smoothness.

The system is being rigorously modeled (using GridLAB-D

and Matlab) in order to derive the optimal control algorithms

and operating parameters that then will be tested in various

configurations to validate or correct predicted models. This

paper describes the results of the modeling so far.

I I . THE ENERGY STORAGE SYSTEM

The energy storage system includes a PV array and a battery

storage system(BESS). The BESS was manufatured by East

Penn Manufacturing Company, and is composed of 3 parts:

the battery system, power conversion system (PCS) and a

controller system. Figure 1 shows the schematic of this system.

A. Photovoltaic Array

The solar PV array produces 500 kW as its peak power

output. Five irradiance sensors collect solar flux information

in different position of the array. This information can be used

to correlate with the weather patterns and/or to provide real-

time date for smoothing algorithms.


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B. Battery Energy Storage System (BESS)

To smooth variations in the output of solar power, the

battery in the storage system needs to be charged and dis-

charged at high rates. Standard Valve Regulated Lead-Acid

(VRLA) battery can not meet the need in such situation. Two

technologies can address this problem. The first technology

used in the smoothing battery is the UltraBattery, which is

a VRLA battery exhibiting ultra-capacitor features for rapiddischarge applications. The second used is the Advanced

Carbon Battery, which is a VRLA battery exhibiting signif-

icantly longer cycle-life than standard VRLA technology and

is used in a shifting application, as opposed to smoothing.

The combination of these two battery technologies enables

long-life VRLA batteries to be deployed with Solar PV power

plants to both smooth power generation that is interrupted by

variable clouds, and shift power generation to times of high

power demand. In this system, the power of the smoothing

battery system is 0.5 MW.

C. Power Conversion System(PCS)

PCS consists of two converters and one inverter. The two

converters are separately connected with shifting and smooth-

ing battery systems in order to convert the DC of battery

into a DC voltage of 600 Volts. Then the two converters are

connected with one inverter which inverts DC into AC. The

PCS collects and logs the data, such as DC voltage, DC current

of the two batteries, and AC current, voltage of inverted battery

output power.

D. Controller System

The controller system is dedicated to derive the active and

reactive power references for the battery systems. At the same

time, the controller system collects all of the information

of the battery systems, and sends the control signal to the

PCS. Controller system gets the information from PCS and

monitoring. The information includes power output and status

of charging for both shifting battery and smoothing battery.

The power reference derived goes to the PCS. The convertors

of PCS use the active power reference to regulate the power

output of battery.

III. SMOOTHING ALGORITHM

Our goal is to use the battery to compensate for the differ-

ence between the power reference and real-time PV output or

the irradiance values. We need to track the PV output or theirradiance values while making the power reference as close

as possible with the real PV output, while making it as smooth

as possible. The moving average algorithm is commonly used

to calculate the power reference.

The only parameter for moving average algorithm is time

interval over which the average is calculated, i.e. the window

size. Different window size will lead to different power

reference with different smoothness.

Independently of the weather pattern, larger window size

leads to a smoother battery output,and more data points will

result in smoother output as well. Hence, a large window

Fig. 2. The irradiance of a cloudy day

Fig. 3. The maximum smoothing power needed

size is an important parameter to get a smooth output. The

window size can be chosen based on the requirement of the

smoothness.

Different window sizes lead to the compensated power

output with different smoothness. So it influences the battery

behavior in following aspects: the maximum charging rate and

discharging rate needed, the status of charging and the ramping

rate needed.

Decisions on the charging and discharging rates will depend

heavily on the weather conditions during a particular day. One

typical cloudy day is used to illustrate our findings. The day

is shown in figure 2 .

A. Maximum Charging Rate and Discharging Rate

The power needed from the battery is equal and less than

the maximum generated PV power, which means the power

needed will not exceed the rating of PV power. In this system,

the PV plant capacity is 500 kW. So the 500kW battery is

enough for this system. Next, we study how much power is

needed in reality. The battery will output power according to

how much power is needed in order to get the best smoothing

result. Figure 3 shows that maximum power ranges around

from 50 % to 70 % of the rated power when the sliding

window sizes are from 10 minutes to 120 minutes. The

maximum power happens when the sliding window size equals

80 minutes.

B. Status of Charging

Figure 4 shows that the status of charging (SoC) varies

with the sliding window size appropriate for this weather. For

this battery storage system project performance is based upon


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Fig. 4. State of charge for different window sizes

Fig. 5. Restoring power

maintaining SoC within a +/- 100 kWhrs while maintaining

an average SoC over a 1 hour period equal to the nominal

SoC. According to this constraint, SoC needs to be maintained

between 10 % and 90 % of the rating 250 kWhrs. Storing

power is needed in addition to the smoothing in order to offset

the battery losses.

C. Ramping Rate

Another important concern with the control of BESS is

the charge/discharge rates (or ramprates), which needs to

be kept under the manufacturers specified values. It is the

rate of change in the instantaneous output from a battery. The

ramping rate is established to prevent undesirable damaging

due to rapid changes in charging or discharge of a battery.

The ramping rate limit of battery is 100 kW/sec. The

ramping rate does not increase as the sliding window size

increases. In the above case, we do not set a limit to the

ramping rate. The ramping rates for a sliding window size

between 600s and 7200s are almost the same. The ramping

rate is in the range of +/-60kw (except for one point). Most

of ramping rates vary between +40kw/s and -40kw/s.

In summary, for this project the maximum charging rate and

discharging rate is large enough for any subset sizes between

10 and 120 minutes. The ramping rates of battery are almost

same for any subset size. The only significant difference is

the change of SoC. The larger subset size will make the SoC

deviate from nominal SoC fast than the smaller subset size do.

A restoring power is needed to maintain the SoC.

D. Restoring Power

Restoring power is used to restore the battery to the nominal

SoC, and, is typically inversely proportional to the SoC at the

moment [3]. The restoring power will change every second

Fig. 6. The comparison of smoothing results between with and withoutrestoring power

with the change of SoC. So the battery output is the sum

of two parts. One is the difference between power reference

and real time PV output, which is used to make the PV output

smooth. Another is restoring power, which is used to maintain

the SoC. Since the restoring power is not smooth, it will

result in multiple smaller spikes within the smoothing output.

In this paper, we use a new method to solve this problem.

The restoring power is calculated each half hour according to

SoC. Since the restoring power is constant for each half hour

instead of every second, it will not influence the smoothing

result. Figure 5 shows the calculated restoring power. Figure 6

shows the smoothing results are almost same smooth for both

cases, but the SoC of battery using restoring power is much

closer to 50 % of rating than not using restoring power. In

summary, the restoring power can help restore the capacity of

battery, but does not affect the smoothness of PV output.

IV. THE COMPARISION OF THREE SMOOTHING

ALGORITHMS

The author has analyzed three smoothing algorithms,

namely: moving average, dual moving average and moving

median. Moving average algorithm calculates the average of

a subset and is well known.

In order to make the output smoother, the moving averagealgorithm can be used twice. But the subset is half of size

which is used for moving average algorithm. For example the

moving average algorithm uses the 600 seconds as the size

of subset, the dual moving average algorithm use the moving

average algorithm twice over the 300 seconds subset. These

two algorithms can have same lag. The result will be smoother

than that from using moving average algorithm once. From

figure 7, we can see the result from dual moving average

algorithm is smoother than moving average algorithm.

From a statistical point of view, the moving median can

track the trend of the PV outputs better than moving average


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Fig. 7. The comparison of smoothing results

Fig. 8. The variation of smoothed PV output

since it underplays a lot of rapid transitions. If the subset

includes a lot of outlying points, the amount of rapid changes

that is not representative for the trend will be taken into

account with the other good data. But moving median tracks

the median for a time series of results, and the rapid changes

are ignored by it. It is more robust in respect to the variations

brought by the clouds.

For these two algorithms, the charging and dischargingrate, ramping rate are in same range. The two differences

among these three algorithms are smoothness and SoC. The

dual moving average algorithm can get the smoothest result

since it uses the average algorithm twice. The moving median

algorithm is the most robust since it ignores the rapid changes.

In order to measure the smoothness, we use the variation

of two consecutive points in smoothed output. From figure

8, we can see that the dual smoothing average algorithm has

the lowest variation. Considering it has same SoC with other

two algorithms, it is the best algorithm among these three

algorithms.

V. HOW TO CHOOSE THE WINDOW SIZE

There is a tradeoff between the smoothness and lifetime of

a battery. The lag between the original figure and smoothed

figure is half of the window size. The larger window size

means smoother result, but also means larger lag. The larger

lag will bring great change of SoC. Consequently, it will bring

big battery energy consumption. The lifetime of a battery is

determined by the cumulative energy used. Therefore, largerwindow size means shorter lifetime of battery. Therefore,

choosing an appropriate window size is a key issue for the

smoothing algorithm

Window size needs to be selected depending on current

weather conditions. If a day is sunny, without any cloud cover,

it may not be necessary to use the battery smoothing system

at all. However, for a cloudy day, the window size should be

selected based on the severity of cloud cover. The relationship

between the window size and smoothness improvement needs

to be explored further. Weather forecasts can be used to adjust

window size based on weather conditions and current energy

priorities. This will be part of our future research.

VI . SUMMARY

A description of a smoothing algorithm is presented, along

with a description of the detailed analysis of the parameters.

Modeling results are presented showing smoothing of the PV

power output. Future work encompasses a complete combi-

nation of smoothing and load shifting with battery as well as

comparing these results with the actual test results from the

site.

REFERENCES

[1] EPRI smart grid demonstration initiative - 3-year update, Electric Power

Research Institute (EPRI), Tech. Rep., Jul. 2011.[2] O. Lavrova, F. Cheng, S. Abdollahy, H. Barsun, A. Mammoli, D. Dreisig-mayer, S. Willard, B. Arellano, and C. van Zeyl, Analysis of batterystorage utilization for load shifting and peak smoothing on a distributionfeeder in new mexico, in Innovative Smart Grid Technologies (ISGT),2012 IEEE PES, Jan. 2012, pp. 1 6.

[3] T. Hund, S. Gonzalez, and K. Barrett, Grid-Tied PV system energysmoothing, in Photovoltaic Specialists Conference (PVSC), 2010 35th

IEEE, Jun. 2010, pp. 002762 002 766.[4] L. Guo, Y. Zhang, and C. S. Wang, A new battery energy storage

system control method based on SOC and variable filter time constant, inInnovative Smart Grid Technologies (ISGT), 2012 IEEE PES, Jan. 2012,pp. 1 7.

Feng Cheng was born in Shanxi, China. She graduated from Beijing JiaotongUniversity in 2007 with the major of power system and automation. Now sheis pursuing her PH.D in electrical and computer engineering in UNM. Herresearch interests are in the area of smart grid and renewable energy.


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Steve Willard P.E. currently serves as the Principal Investigator for PNMsSmart Grid Storage Demonstration Project with the Department of Energy. Hehas more than 25 years experience in the energy industry in regulated and un-regulated markets, including product development and support, energy systemengineering and analysis as well as energy industry market research. Previouspositions include Manager of the Center for Innovation and Technology atPNM, Product Support Manager for Honeywell Power Systems, Lecturer inthe US Peace Corps and Computer Applications Engineer at Bridgers andPaxton Consulting Engineers Inc. Steve holds 2 US Patents, BSME and MBAdegrees, both from the University of New Mexico, and is a licensed engineer

in the State of New Mexico.

Jonathan Hawkins is the Manager of Advanced Technology and Strategyat PNM Resources, an energy holding company based in Albuquerque NewMexico. Jonathans team is responsible providing research and developmentof new technologies and the proposal of possible business applications ofemerging technologies in support of PNM Resources strategic objectives. Ar-eas of responsibility include smart grid technologies and strategy, integrationof distributed energy resources; plug in hybrid electric vehicles, and storagetechnologies. Jonathan Hawkins received his Bachelor of Science degree inElectrical Engineering from the University of New Mexico in 1994. Aftergraduation, he went to work for Sumitomo Sitix Silicon, Inc. as an engineerresponsible for semiconductor pre and post production material characteri-zation. Jonathan joined PNM Resources in 2002 where he managed PNMsDistribution Standards organization, which provides material specificationsand model standards for design and construction of utility infrastructure. In2010 he became the Manager of the Advanced Technology group. Jonathancurrently sits on the Electric Power Research Institutes (EPRI) ResearchAdvisory Committee as well as research program advisor roles for EPRI;the Smart Grid Interoperability Panel (SGIP) as PNMRs voting memberand member of the Distributed Renewable, Generators, and Storage DomainExpert Working Group; and is a member of the Institute of Electrical andElectronics Engineers (IEEE).

Brian Arrelano was born in Farmington, New Mexico, on August22, 1974.He graduated from the University of New Mexico in 2006 with a Bachelorsof Science in Electrical Engineering. His employment experience includesGeographic Information Systems Technical Supervisor with Public ServiceCompany of New Mexico (PNM), Distribution Engineer with PNM, Santa FeDivision in Northern New Mexico, and continuing as an Advanced TechnologyProject Manager with PNM Resources. His special fields of interest includesmart grid technology in the utility industry along with process improvementsusing Lean and Six Sigma Methodology. He is currently working on anEnergy Storage Research and Development Project supported by the DOE.Project partners of this project include the University of New Mexico, aswell as Sandia National Laboratories providing support for data modelingand analysis.

Olga Lavrova was born in St.Petersburg, Russia in 1974. She receivedher B.Sc degree in Physics and M.Sc. degree in EE from the St.PetersburgState Electrical Engineering University, and her Ph.D. degree from UCSB in2001. Her employment experience includes post-doctorate research at UCSB,as well as working in the areas of optoelectronic devices at two start-upcompanies and a major corporation (Emcore Corp). She joined University ofNew Mexico in 2007 as a Research Professor, and is now Assistant Professorat the Electrical and Computer Engineering Department. Her current work andareas of interest include photovoltaics and nano-scale semiconductor structuresfor photovoltaic applications, Smart Grids and emerging energy generation,distribution and storage technologies.

Andrea Mammoli was born in Ancona, Italy on April 18, 1968. He graduatedwith a Bachelor of Engineering in 1991, and with a Ph.D. in 1995, fromthe Department of Mechanical & Materials Engineering at the Universityof Western Australia. was a Director Funded Postdoctoral Fellow at LosAlamos National Laboratory from 1995 to 1997. He subsequently joined theUniversity of New Mexico as a research faculty member, and is now AssociateProfessor in Mechanical Engineering, and co-Director of the Center forEmerging Energy Technologies. His current research deals with the integrationof building-scale energy systems with the electricity grid, particularly asapplied to energy storage and distributed systems management. Mammoli is

Regents Lecturer and Halliburton Professor at the University of New Mexico.His projects received several awards, including the Association of EnergyEngineers Region 4 Renewable Energy Project of the Year in 2009 and theGridWise Architecture Councils GridWise Applied Award in 2008.

This report was prepared as an account of work sponsored

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applying battery energy storage-171

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