2012 3rd IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), Berlin

Optimal Storage Capacity within an

Autonomous Micro Grid with a high Penetration

of Renewable Energy Sources P. Lombardi, T. Sokolnikova, K.V. Suslov Member IEEE, Z. Styczynski Senior Member, IEEE,

Abstract--Some Renewable Energy Sources (RES), such as

wind and solar, produce power intermittently according to the

weather conditions rather than to the power demanded. Energy

Storage Systems (ESS) may be used to mitigate the intermittent

generation from RES and to increase the quality of power supply.

This study aims to find the relationship between the

generation from RES and the needed amount of ESS. An

autonomous microgrid has been analyzed in which a part of

electricity demanded during one year is generated by Wind

Turbines and Photovoltaic plants (PV), while the remaining part

is produced by fuel based generators. An intelligent Energy

Management System (EMS) optimally schedules the fuel based

generators according to the load demanded, the weather

conditions and to the electricity generation costs. The optimal

storage needed to balance the system for different scenarios were

investigated and results of this investigation are shown and

discussed in this paper.

Index Terms--Autonomous microgrid, energy storage systems,

mixed integer linear programming, renewable energy sources,

smart grid.

I. INTRODUCTION

RENEWABLE Energy Sources (RES) are candidates to be

the backbone of the future power systems. However some

RES, such as wind and solar, generate power not when it is

demanded but in an intermittent and variable way. This makes

it difficult to integrate the power generated from these RES

into the electric network. In order to solve this problem

smarter power structures (Smart Grids) are going to be built

[1]. Smart Grids are mainly based on Information and

Communication Technologies (lCT) and on Energy Storage

Systems (ESS). Among the Smart Grids, different structures

like micro grids or Virtual Power Plants (VPP) are going to be

developed. One of the main characteristics of these structures

is the possibility to operate autonomously [3], [10]. In an

autonomous power system with a high penetration of RES the

problem coming from the intermittent generation has to be

P. Lombardi is with the Fraunhofer Institute for Factory Operation and Automation IFF, 39106 Magdeburg Germany (e-mail: pio.lombardi@iff.fraunhofer.de).

T. Sokolnikova and K.V. Suslov are with the Department of Power Supply and Electrical Engineering of Irkutsk State Technical University (ISTU), 664074 Irkutsk Russia (e-mail: souslov@istu.edu ).

Z. Styczysnki is with Otto-von-Guericke-University Magdeburg, 39 \06 Magdeburg Germany (e-mail: sty@ieee.org).

locally solved. Generally Demand Side Management programs

(DSM) as well as ESS are the most commonly used solutions.

However the contribution of DSM is marginal and it has a low

influence on the optimal energy storage capacity [4].

This study aims to search for the relation between the

needed storage capacity and the amount of energy generated

by wind and solar within an autonomous microgrid. The

microgrid is composed by four fuel based generators, a small

wind farm and a PV plant. An intelligent Energy Management

System (EMS) optimally controls the fuel based generators

and the ESS with the aim to minimize the fuel costs. Eleven

main scenarios were analyzed. In the first and last scenario the

amount of electricity generated by wind and PV is zero and

100%, respectively. Moreover, sensitivity analyses were

carried out to fmd the influence between the type of RES

based technology that was used and the energy storage

capacity.

II. AUTONOMOUS MICRO GRID: MODELING AND PROBLEM FORMULATION

A. Micro grid description and modeling of the installed power

Microgrids are defined as a group of generators, energy

storage systems and loads which, in part, can be also

controlled [5]. The generators may use RES such as wind,

solar and biogas, or clean fossil sources such as natural gas.

Generally the generators that burn gases produce both

electricity as well as thermal energy. The combination of both

types of generation has the advantage to increase the overall

efficiency by recovering of the energy of the exhausted gases.

Energy storage systems are mainly used for power quality and

energy storage applications. Flywheels and supercapacitors are

generally used for power quality applications. Batteries are

mostly devoted for energy management application even if

some high temperature battery technologies, such as NaS, can

be used both for energy management as well as for power

quality purposes [6]. With regard to the loads, some of them,

like heating, air conditioning or wash machines, can offer to

possibility to be controlled using load management programs

which may decide to shift the load or to curtail them during

particular conditions [7] . In order to control all the generators,

energy storage systems and load information need to be

evaluated and sent to a central Energy Management System.

For micro grids as well as for the entire smart grid concept

ICT are the backbone on which the system depends.

978-1-4673-2597-4/12/$31.00 ©20 12 IEEE

I

2

In this study an autonomous micro grid was analyzed. The TABLE II:

micro grid is composed of four conventional generators, a INSTALLED POWER IN EACH SCENARIO

wind farm and a PV plant (Fig. 1 ). An EMS optimally controls

the production of the four conventional generators according

to their generation costs, to the weather conditions and to the

State of Charge (SOC) of the storage system. Besides the

control of the generators the EMS also controls the loads. If

the generators are not able to cover all of the demand, the

EMS curtails a part of the load.

Normalized curves were used to model both the generation

profiles of the wind farm and PV plant as well as to model the

load profile. These curves were measured in a wind farm, a

PV plant and in a district all situated in Germany. The values

of the full load hours for the used profiles are shown in Table

I.

Scenario

I 1(0%) II (10%)

I III (20%) IV (30%)

I V (40%) VI (50%)

I VII (60%) VIIl(70%)

I IX (80%) X (90%)

! XI (100%)

Gen.1 [MW]

0.25

0.225 0.2

0.175

0.15 0.125

0.1

0.D75 0.05

0.025 0

Gen.2 Gen.3 [MW] [MW]

0.25 0.25

0.225 0.225 0.2 0.2

0.175 0.175

0.15 0.15 0.125 0.125

0.1 0.1

0.075 0.075 0.05 0.05

0.025 0.025 0 0

Gen.4 Wind PV [MW] [MW] [MW]

0.25 0 0 I 0.225 0.18 0.2

0.2 0.23 0.4 I 0.175 0.3 5 0.61

0.15 0.47 0.81 I 0.125 0.59 1.02

0.1 0.7 1.22 ! 0.075 0.82 1.43 0.05 0.94 1.63 I

0.025 1.06 1.84 0 l.l8 2.04 !

TABLE T : The installed power of the energy storage system was

FULL LOAD HOURS FOR THE USED PROFILES calculated as the difference between the sum of the installed

Wind farm PV plant Load [hours] [hours] [hours]

Full load hour 1580 912 3 723

Fig.l Scheme of the analyzed micro grid

The micro grid has a maximal and a minimal power

demand of 1 MW and 0.136 MW, respectively, while the

yearly electricity demanded is 3723 MWh. Eleven scenarios

were simulated in order to check the relationship between the

generated electricity from RES and the needed storage

capacity. In the first scenario the energy demanded in one

year is totally generated by the four conventional generators,

while in the last scenario it is completely generated by the

wind farm and the PV plant. In the first scenario no ESS was

considered. In the other nine scenarios a part of the

demanded energy is produced using RES while the rest is

generated using the four conventional generators. The energy

produced by RES is equally divided between the wind farm

and the PV plant. Table II describes the installed power for

each technology in each scenario.

I

RES power and the minimal demanded power (1). For the

considered generation profile of wind and PV the installed

power coincides also with the maximum generated power. The

application of the storage is mainly dedicated to the energy

management rather than to the grid support. In fact, it has to

compensate the intermittent generation of the RES based

plants. No specific ESS were considered, however an overall

efficiency of 81% (T\ch=T\disch=0.9) was assumed, which is

typical of some battery technologies.

Pstorage = (Pinscwind + Pinscpv) - min(Pload) (1)

B. Optimal scheduling of generators

As mentioned above, the EMS optimally controls the

conventional generators for minimizing the fuel costs. A

quadratic cost function was used which relates the fuel cost to

the generated power (2). Table III shows the data of the

generators.

C(P) = a ' p2 + b . P + c (2)

TABLE Ill:

DATA OF GENERA TORS

Generator Pmax Pmin Fuel cost coefficients Start [MW] [MW] up

costs [€]

a b c [€/MW2h] [€/MWh] [€/h]

1 * 0 0.01 3 0 109 28

I 2 * 0 0.023 42 97 28 3 * 0 0.026 3 2 109 28

I 4 * 0 0.024 97 100 28 I *See Table II

Since the mixed integer linear programming algorithm was

chosen to optimally schedule the generators, the cost functions

were linearized. Therefore, instead of a quadratic function,

piecewise linear functions were calculated. The objective

function as well as the constrains of the optimization problem

I I

I I

3

are shown in (3) and (4), respectively. 4

OF1 = min L CJUi' Pi) i=l

RES the needed storage capacity increases too (Fig.2). Fig.2

also shows that if the RES share is lower than 10%, the total (3) annual costs linearly increase with increasing energy storage

capacity.

{t Pitt) � LDad(t) - Pwin,(t) - pp.(t) ± P,h,'", (4)

Ui . pimln :::; Pi :::; Ui . Pimax

Where • C, are the generation costs, •

Ui is a binary variable [0 or 1], (1 if the generator

is set up) • P, is the produced power from the generator i, • t is the time step (one hour), • Load is the demanded load for the hour t, • PlI'ind is the power generated during the hour t by

the wind farm, • Ppl' is the power generated during the hour t by the

PV, • Pclt/disclt is the power charged and discharged

to/from the ESS during the hour t. Such a power is

positive when the ESS is charged and negative

when it is discharged.

C. Optimal storage capacity

One of the main aims of this study is to find the optimal

storage capacity for the analyzed micro grid. In order to fmd it

a second optimization problem was set. The problem consists

in minimizing the total annual cost of the micro grid. The

annual costs are defined as the swn of the fuel costs (Costfilet), the discounted investment costs of the ESS (CostInI'Ball)' the

costs for switching off the load if all the generators are not

able to cover it (CoStslI'lIchojj) and the costs for not producing

RES power if the EES is full (CostslIrptllsRES) (5).

OFz = min( Cost!Uel + CostinvEEs + CostswitChO!! + CostsurPlusRES)

(5)

The investment costs for the ESS were assumed to be 2500

€/kWh. A life time of 10 years and a discount factor of 10%

were considered. The costs due to the switch off of the loads

were estimated using the Value of Lost Load (VOLL). This

value depends on the kind of load that should be switched off.

For industrial and commercial loads it ranges between 10000

and 40000 € per MWh [9],[2]. In this study 10000 € per MWh

was assumed as VOLL. Finally, the authors assumed that the

costs for not producing from RES may be estimated as 200 €

per MWh.

III. RESULTS

Eleven scenarios considered, as shown in Table II above. In

these scenarios the energy generated by the RES is equally

divided between the wind fann and the PV plant. The total

annual costs in each scenario were evaluated. The analysis

shows that by increasing the amount of energy generated by

X 10"

16.-----------.------------.-----------.

14 ...................... � ........................ : ...................... .

· . · . · . · . o 12 --------------------- �------------------------�-----------------------

:s : : � 10 -- ------------------- �------------------------l-----------.------.----

o . .

o : : gJ 8 · ·· ................ j ........................ ; ...................... .

c : : c . .

ro

co

(5 f- 4 ----------------------.-----------------------

• 0

2 ....................... •................

200/, 100/, O L-----------�----------�----------� o 5 10 15

Storage capacity[hl Fig.2 Total annual costs related to the RES share and to the storage capacity

In this study, investing in ESS became profitable only if

more than 10% of the annual electricity is produced by RES.

The reason mainly depends on the assumed VOLL. If this

value is increased, the profitability to have a storage system,

even when the share of RES is lower than 10%, increases too.

If the micro grid is fully fed through RES, a storage system

with an installed power of 3 MW and a capacity of 7.6 hours

results as the optimal choice (Fig.3).

u

ro

o " , , . . . . , til ; ; ;

40L-�10--�2� 0--�3�0 --�40---5�0--�6�0 --�70---8�0--�9�0�100 Generation from RE S[%]

Fig.3 Optimal storage capacity for different RES generation

The sensitivity analysis showed how the technology used to

generate electricity influences the storage capacity. If the

micro grid was fully fed using wind turbines, a storage

capacity of 57 MWh is required. However, if the micro grid

was fed only through PV plants, the storage capacity

decreased to nearly 17 MWh, as shown in FigA.

Sensitivity analysis

......... wind share 0% PV share 100% in RES

-- wind share 50% PV share 50% in RES . . . ,

_._.

_.- wind share 100% PV share 0% in RES ····c··········j·

, {/ :;;; . . . . .1

� 40 .... -... -.... ; ... --......... � ...... - -..... ; ...... -....... � ..... / .... . u : ' , : / � : : /"

ro : , I : ;"

� 30 ····-----···- 1----···------+·-----····--- �-···-----··.�.�+---.... -----ro ' I I J I

i :: ••••••••••••• ] ••••••••• _-L�,:,T���J,�/-_

. . -._."" . .-

o 20

Fig.4 Sensitivity analysis

60

Generation from RES[%]

IV. CONCLUSIONS

80 100

The influence between the energy generated using RES based technologies and the optimal storage capacity was

analyzed within an autonomous micro grid structure. The

analysis shows that the optimal storage capacity mainly

depends on three factors: • Amount of energy generated by RES, • Type of RES technology , • Value of Lost Load.

A low VOLL justifies the use of energy storage systems

only if the share of RES is higher than 30%, while due to the

generation profile of the PV plant such technology requires a

higher storage capacity.

In further research the autonomous system with a grid

connection will be investigated for minimizing the cost

function.

V. ACKNOWLEDGMENT

The authors gratefully acknowledge the fmancial support of

the Russian Federation in the scope of the Grant 220 and the

contributions of Miss Xiubei Ge for her work on the original

version of this document.

VI. REFERENCES

Periodicals:

[I] S. Mossoud Amin, 'Toward a smart grid: power delivery for the 21st century". Power and Energy Magazine. IEEE., 2005

[2] K. K. Kariuki, R. N. Allan, "Applications of Customers Outage Costs in System Planning, Design and Operation" IEEE Proceeding -Generation, Transmission and Distribution 143 ,3 05-312,1996

Papers from Conference Proceedings (Published):

[3 ] P. Lombardi, P. Vasquez, Z. Styczynski, "Optimised autonomous power system" in Proc. 2009 Cigre iEEE PES Joint Symposium Calgary, 29 July 2009.

[4] P. Lombardi, M. Stlitzer, Z. Styczynski, A. Orths, "Multi-criteria optimization of an energy storage system within a Virtual Power Plant architecture" in Proc. 2011 iEEE POlVer Engineering Society General Meeting Conj,24-29 July 2011 Detroit.

[5] C. Marnay, O.c. Bailey. "The CERTS Microgrids and the future of microgrids", Berkeley, California 2004.

4

Technical Reports:

[6] EPRI-DOE,"Handbook of energy storage for Transmission and Distribution applications", 2003 .

[7] N. D. Hatziargyriou, A. Dimeas, A. G. Tsikalakis, l Oyarzabal, lA. Pecas Lopes, G. Kariniotakis, "Management of microgrids in market environment", 2009, Available: http://www.microgrids.eu/micro2000/presentations/3 9.pdf

[8] Styczynski Z., Lombardi P at allii . "Electric Energy Storage Systems". Electra 255, April 2011", CTGRE Paris ISBN: 978- 2- 85873 - 147-3

[9] CRA International "Assessment of the Value of the Customer Reliability (VRC)", August 2002.

Dissertations:

[10] P. Lombardi, "Multi criteria optImIzation of an autonomous virtual power plant". Ph.D. dissertation, Res electricae Magdeburgenses, Bd. 38, ISBN 9783 940961556 3 94096155

VII. BIOGRAPHIES

Pio Lombardi studied mechanical engineering at the Politecnico di Bari, Italy. He graduated in 2006 at the same university with the degree M.Sc. He joined the Chair of Electric Power Networks and Renewable Energy Sources at the Otto-von-Guericke University Magdeburg, Germany as a research engineer in 2006. At the same university he received his PhD. In 20 II he joined the Process and Plant Engineering of Fraunhofer Institute for Factory Operation and Automation IFF. His primary field

of interest includes modeling, simulation and optimization of Smart Grids. He is a member of the Baikal project research group.

storages.

Tatyana V. Sokolnikova graduated in 1985 with M.Sc. from the Irkutsk State Technical University (ISTU) in Hydrogeology. Between 1985 and 2005 she was a leading planning engineer in the Planning Institute Irkutsk. In 2008, she completed her master's degree in Smart Grid technology at the ISTU and is now working in the scope of the Bajkal Projekt on her Ph.D. Her research interests are related to the planning and optimization of autonomous Smart Grids, taking into account the role of energy

Konstantin V. Suslov is an Associate professor at the electric power supply department of Irkutsk State Technical University. He graduated from the Irkutsk State Technical University with the specialty "electric drive and industrial automations". He has a candidate of science degree in technics. His research interests are related to computer engineering and automation, information-measuring equipment in automated accounting systems of power consumption. He is a

project research group.

Zbigniew A. Styczynski (SM '01) received his PhD in EE at the Technical University of Wroclaw. He worked at the Technical University of Stuttgart, Germany and 1999 he became Chair of Electric Power Networks and Renewable Energy Sources of the Faculty of Electrical Engineering and Information Technology at the Otto-von­Guericke University, Magdeburg, Germany. Since 2006 he has also been the president of the Centre of the Renewable Energy Saxonia- Anhalt, Germany. His

special field of interest includes modelling and simulation of the electric power networks systems, renewable, and optimization problems. He is the author of more than ISO scientific papers, a senior member of IEEE PES, a member of CIGRE SC C6, VDE ETG and IBN and a fellow of the Conrad Adenauer Foundation. In 2011 he won the Super Grant of the Russian Federation together with the Irkutsk State Technical University (Project Baikal) and is leading a research group at ISTU in the scope of Smart Grid.