[IEEE 2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER) -...

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2014 Ninth International Conference on Ecological Vehicles and Renewable Energies (EVER)

Characteristics of Energy Storage in Smart Grids

Jan-Hendrik Psola, Markus Henke IMAB, TU Braunschweig

Hans-Sommer-Str. 66 38106 Braunschweig Germany

Email: [email protected], [email protected]

Kritika Pandya Electical Engineering

Malaviya National Institute of Technology Jaipur, India

Email: [email protected]

Abstract—The paper considers the impact of fluctuating energy generation by renewable energy resources within low voltage grids. Hereby the sizing and positioning of energy storage will be determined in order to obtain a smart grid. Furthermore suitable storage technologies will be discussed briefly.

Keywords—energy storage; grid integration; renewable energy; energy distribution.

I. INTRODUCTION

In recent years the proportion of renewable energy generation has been rapidly increasing. This has an impact on the grid situation in general as well as on the conventional energy generation.

In particular it is the low voltage grids (400 V) where the most installed photovoltaic capacity can be found. This capacity is distributed and decentralized within all low voltage networks. As the load in the networks having this installed photovoltaic energy sources, which are mainly residential and rural networks, is less in general these networks become energy sources. This occurs mostly during summer at noon, however according to the installed capacity off-peak hours and spring/autumn seasons are effected as well. Therefore according to their net-balance these networks may act as power plants and deliver energy to the upper grid level, e.g. 20 kV level.

The main problem in the low voltage grids is the voltage level. Whereas the transformer capacity limit will not be overrated. In order to keep this voltage according to the grid code under 1.1 p.u. the inverter of the photovoltaic facility change their output from active to reactive power at a certain point. This will balance the voltage level; however less energy will be produced and delivered to the upper energy grid [1], [2], [3].

In order to maximize the energy generation and still stabilize the voltage level according to the European grid code between 0.9 and 1.1 p.u. voltage innovative approaches need to be considered. Therefore the use of energy storage in the low-voltage grid will be discussed.

Hereby besides the capacity and rated power the main focus is on the place where this storage should be installed to generate the most impact. Therefore different operating modes will be researched as well.

II. ENERGY STORAGES

A. Requirements In order to select a certain energy storage technology

for the low-voltage level operation the requirements need to be identified. There are several different technologies and systems to store electrical energy, thus they all are useful for different operation modes [4].

The energy storage will be connected to the 400 V low-voltage grid, thus the technology should be connected directly instead of using an additional transformer. Furthermore the connection will take place near buildings and under existing grid situations. Therefore the specific volumetric energy- and power-density is important. The technology should also be free of geographical requirements.

As the energy storage must be adapted to certain load situation it must be scalable in small steps in energy and power rating. Hereby a technology should be selected that offers medium energy capability.

Finally a technology with a low self-discharge rate is preferable. Additionally the cost per unit energy- and power-rating should be minimal.

B. Selected Technology Energy storages which are based on chemical

substances are not applicable, as they will be used for high energy and high power applications. Also CAES and pumped hydro are not applicable due to high power application, and they require special geographical conditions which cannot be guaranteed in all low-voltage grids. Furthermore these technologies are not scalable in the required step-size.

Super-capacitors and super conducting magnetic energy storages are not able to store enough energy in order to keep the self-discharge rate low.

The remaining technologies are battery energy storages as Lead-acid, Li-Ion, Sodium-sulfur and Redox-Flow. Battery energy storages are easily scalable, and do not require certain geographical conditions. It is also possible to subdivide storage into several small ones, which is one part of our investigation.

As residential and rural grids are in the focus the sodium-sulfur batter may not be used due to the problematical circumstances in case of a fire.

The three remaining batteries are all suitable; however a technology with a high volumetric energy density and low self-discharge rate is preferred. Therefore the Redox-Flow battery will be chosen. This technology has also the advantage over other battery technologies that the rated power and energy capacity can be chosen independently [5]. Battery cost and maintenance will be discussed in further research.

III. MODEL

In this section the model which is used for simulation purposes is described in detail. The model is developed in MATLAB/Simulink.

A. Grid For the purpose of study, a 400 V three phase

reference grid has been used based on the topology of European low-voltage distribution benchmark network [6]. A schematic diagram of this three phase distribution feeder with overhead lines is shown in Fig. 1.The low voltage network is connected to 20 kV supply via a MV/LV distribution transformer and has a radial structure. This ∆-Y grounded transformer, (Node N0, figure 1) rated 300 kVA, is presumed to be unaffected by the power level fluctuations in the circuit. The geometrical dimensions and the parameters of overhead lines in the feeder are according to the benchmark network specifications. Each overhead line segment is 30 meters in length. The system frequency is 50 Hz.

Load data, in 15 minute time steps has been used from a set of electricity consumption data of 100 representative households in Braunschweig, Germany for the year 2009. The three phase residential load is balanced and is assumed to have a power factor of 0.85. The values of coincident peak loads per phase for winter, summer and spring corresponding to the nodes marked in figure 1 are tabulated below in table 1.

Fig. 1: Structure of analyzed low voltage grid

TABLE 1: LOAD DATA AT NODES

Node Maximum Load [kVA]

Spring Summer Winter Power Factor

N1 9.394 9.347 10.962 0.85

N2 4.715 5.462 4.715 0.85

N3 10.519 11.266 13.966 0.85

N4 8.854 9.359 8.794 0.85

N5 5.471 4.904 4.535 0.85

N6 14.341 13.026 13.716 0.85

N7 9.670 9.646 11.099 0.85

B. Photovoltaic For photovoltaic a total installed rating of 177 kWp

has been assumed and this capacity is distributed throughout the grid. Photovoltaic rated 30 kWp, 45 kWp, 30 kWp and 72 kWp have been paced at nodes designated as N2, N3, N4, and N7 in figure 1. In order to have realistic fluctuation in photovoltaic energy generation behavior of the system weather information has been used. Therefore nominal photovoltaic output data based on field measurements per 15 minute time steps for 2009 in Braunschweig is used. As the grid span can be neglected same nominal data is chosen for all photovoltaic systems. The dc output of photovoltaic is

connected to the grid via an inverter whose efficiency is assumed to be unity in order to neglect inverter losses.

Monday Tuesday Wednesday Thursday Friday Saturday Sunday0

0.5

1

Days of the week

Nom

inal

phot

ovol

taic

outp

ut NOMINAL PHOTOVOLTAIC OUTPUT IN A WEEK IN JANUARY

Fig. 2: Nominal photovoltaic power in winter


0.5

1

Days of the week

Nom

inal

P

hoto

volta

ic

Out

put NOMINAL PHOTOVOLTAIC OUTPUT IN A WEEK IN APRIL

Fig. 3: Nominal photovoltaic power in spring


0.5

1

Days of the week

Nom

inal

P

hoto

volta

ic

Out

put

NOMINAL PHOTOVOLTAIC OUTPUT IN A WEEK IN JULY

Fig. 4: Nominal photovoltaic power in summer

C. Photovoltaic For A Redox-Flow battery model is adapted from a

previous research [5]. However the model was modified according to the spectated time steps. The model is based on measurements on a Redox-Flow battery system which is rated 10 kW/100 kWh. During this investigation it could be found out that the actual capacity is higher and that 100 kWh is the nominal working capacity, i.e. this can fully be discharged. In general battery systems will not be discharged lower than 10-20 % SOC according to the technology. This is to prevent deep discharge in order to achieve higher life time of the system. In our approach we will adopt this and the battery SOC might become zero, yet there will be no deep discharge.

Redox-Flow batteries have little self discharge during standby, this battery has 150 W. As it can be assumed that the battery will be most of the time in operation mode we will neglect this loss. Inverter losses are also not taken into account according to the PV model.

The previous research showed a cycle efficiency of about 75.2 %. In this approach we divide the cycle efficiency ηcycl into a charge and discharge efficiency (ηch, ηdch) as in Eq. (1). Furthermore both efficiencies are assumed to be equal, so they can be calculated according to Eq. (2).

ηcycl = ηch ⋅ ηdch = 75.2 % (1) ηch = ηdch = √ηcycl = 86.7 % (2)

This will result into a realistic approach and the losses will not suddenly appear during discharge. In order to achieve different battery storages ratings multiples of 10 kW/100 kWh will be used.

IV. SIMULATION

A. Scenarios For the year 2009, the household load and nominal

photovoltaic output data pertaining to the first week in the months of January, April and July has been selected for examining the behavior of the grid in winter, spring and summer respectively. After a series of simulations with different test battery capacities, an appropriate battery storage system rating of 200 kWh and 20 kW is defined and has been kept constant during the study. This battery size offers the best results, while keeping its influence moderate according to the grid situation.

In order to find out where in the grid the battery energy storage system gives best results, three representative positions of storage are chosen and investigated. In one case, battery storage of full rating has been placed near the 20 kV/400 V step-down transformer (Node N0, figure 1). In another situation, battery with full rating has been placed at the extreme end of the radial feeder, which is also the longest leg of the grid (Node N7, figure 1). Distributed storage has been examined as the third case. Here two batteries are used in the middle of the grid (Nodes N1 and N5, figure 1), each with half the original capacity, i.e. 100 kWh and 10 kW.

The energy content of the battery at the beginning of the week is presumed to be fifty percent of its full capacity. This assumption is adopted in order to have a more realistic and unbiased ascertainment of the advantage of using battery storage system to improve grid voltage level in any random week of the year.

Grid voltage behavior has been examined under different control schemes and operating constraints for battery system. Figures 5 to 7 show the different operating modes according to the per unit voltage. If the power is positive that means the battery will be charged and act as a load, while negative power means the battery storage will deliver energy to the grid and discharges. These scenarios are enlisted symbolically in table 2.

TABLE 2: SELECTED SCENARIOS

Storage Position in

Grid

Scenarios

Spring Summer Winter

Near Source N, S, NDPV, SDPV

N, S, N(1.02), NDPV, SDPV


Near the End

N, S, NDPV, SDPV



At two Places in the

Middle

N, S, NDPV, SDPV



N: Normal battery function used, with reference voltage as 1 p.u.; S: Special battery function used, with reference voltage as 1 p.u.; N(0.98): Normal battery function used, with reference voltage as 0.98 p.u.; N(1.02): Normal battery function used, with reference voltage as 1.02 p.u.; NDPV: Normal battery function used, with twice the original installed photovoltaic rating and reference voltage as 1 p.u.; SDPV: Special battery function used, with twice the original installed photovoltaic rating and reference voltage as 1 p.u.;

Fig. 5: Normal battery function

Fig. 6: Battery functions with reference p.u. of 0.98 and 1.02

Fig. 7: Special battery function

In addition to these, grid models without any battery storage for each of summer, winter and spring have also been investigated. They serve as the basis of comparison and help in analyzing the extent of usefulness of employing the battery system. Simulations of the low-voltage grid sans storage have thus been carried out for the original photovoltaic installed capacity as well as after doubling the photovoltaic rating.

Per unit line voltage is measured at four extreme corners of the grid (Nodes N0, N1, N5 and N7, figure 1). Measurement is carried out for every 15 min time-step of the scenario data, leading to 672 data points in one respective week. For each of these above four points, and for every scenario in table 2, per unit voltage variation over the week for winter, summer and spring was studied for the following four situations:

a) No battery storage is used b) Battery is placed near the supply side c) Battery is placed at end of the grid d) Distributed storage in the middle of the grid

B. Results a) Behavior of Storage at the end of the grid in

spring with p.u. function

Figure 8 shows that an energy storage does make an impact on grid voltage of our scenario already in spring time, it can be seen that the storage can level the p.u. voltage. The best results for the grid are achieved if the energy storage will be located at the end of the grid, i.e. the longest leg.

The storages will always make the biggest impact at their place of installation. Therefore storage at the transformer will have little effect as the grid voltage at this point will always be leveled due to its close distance to the upper grid level coupling point. Furthermore the

storage will behave according to the voltage level at installation place, which is already quite stable near the transformer.

Monday Tuesday Wednesday Thursday Friday Saturday Sunday0.95

1

1.05

1.1

1.15

Days of the Week

Per

Uni

t V

olta

ge

No battery storage usedOne battery storage used near sourceOne battery storage used at the end of gridTwo battery storages placed in middle of grid

Fig. 8: Per unit voltage at the end of the grid in spring with normal battery function and reference of 1 p.u.

b) Storage at the end of the grid in summer where 1 and 1.02 p.u. reference was used

In this approaches a variable p.u. voltage control according to season was adapted in order to achieve better results in storage use. Figure 9 shows results for summer at the end of the grid where as benchmark a targeted p.u. voltage of 1.0 was used. In the second case a targeted p.u. of 1.02 was used in order to achieve some reserve in the storage when high p.u. will appear (figure 10).

A similar approach for winter with targeted p.u. of 0.98 showed similar results. However due to low photovoltaic generation and relatively moderate load this showed little improvement.


1

1.05

1.1

1.15

Days of the Week

Per U

nit V

olta

ge

Fig. 9: Per unit voltage at the end of the grid in summer with normal battery function and reference of 1 p.u. (labelled according to Fig. 8)


1

1.05

1.1

1.15

Days of the week

Per U

nit V

olta

ge

Fig. 10: Per unit voltage at the end of the grid in summer with normal battery function and reference of 1.02 p.u. (labelled according to Fig. 8)

c) Storage at the end of the grid in summer with twice photovoltaic. Advanced storage function based on SOC was used.

In this approaches a variable p.u. voltage control according to the SOC of the battery was adapted according to figure 7.

The figure 11 and figure 12 show the results of the normal and special control method with doubled photovoltaic. Photovoltaic was doubled in order to have a future based results and to generate more stress for the grid. In figure 13 and figure 14 the energy content of the battery is shown respectively. In case of normal function the storage will be fully charge during longer periods than with the special battery function. This will result in higher peak p.u. voltage. With the control according to SOC the battery will, if charged over 75 %, discharge with higher rates if possible. Hereby the energy storage will be longer available if needed during peak times.


1

1.05

1.1

1.15

Days of the week

Per

Uni

t V

olta

ge

Fig. 11: Per unit voltage at the end of the grid in summer with twice photovoltaic, normal battery function and reference of 1 p.u. (labelled according to Fig. 8)


1

1.05

1.1

1.15

Days of the week

Per u

nit v

olta

ge

Fig. 12: Per unit voltage at the end of the grid in summer with twice photovoltaic, special battery function and reference of 1 p.u. (labelled according to Fig. 8)


1

1.2

1.4

1.6

1.8

2x 105

Days of the Week

Bat

tery

Ene

rgy

(kW

h)

Fig. 13: Battery energy with normal battery function


1

1.2

1.4

1.6

1.8

2x 105

Days of the Week

Bat

tery

Ene

rgy

(kW

h)

Fig. 14: Battery energy with special battery function

Similar results were achieved during winter as the battery will have better ability to provide energy for the grid when the battery will charge at higher rate. However, as in other cases, the results for winter do not have such

strong impact as the photovoltaic generation in moderately low.

TABLE 3: ENERGY/POWER VARIATION

Case Power Fluctuation Outcome drawn from Upper Grid

max(P)d min(P)d mean(P)d std(P)d E/E0c

No Storage

43.5 -281.3 -65.0 93.6 1

NBFa 35.4 -281.3 -63.8 88.3 0.982

SBFb 42.1 -262.8 -62.3 82.3 0.958

a. Normal Battery Function; b. Special Battery Function; c. Energy delivered to upper grid normalized by energy to upper grid when no storage is used; d. in kW

V. CONCLUSION The research has shown that energy storages can

make an impact on the p.u. voltage of a low voltage grid with distributed energy generation. It has been shown that the storage has more influence on the voltage in the grid if it is located at the end of the grid or the longest line, i.e. it is placed far away from the transformer.

For a relatively small low voltage grid, battery storages are the most favorable technology. Battery storages can be scaled freely as well as placed at certain points within the grid.

From the investigated operation schemes the special battery function showed the best results. Hereby the SOC of the battery is also considered. As the power output of the battery depends on the storage energy content in addition to the p.u. voltage, the battery storage will be able to work for longer periods. This is because the storage will not be as easily full or empty as with the normal battery function.

The oscillation behavior in figure 12 might be caused due to the higher battery rating, i.e. there was no limit in dP/dt change which results in oscillation of p.u. voltage at a certain point. The normal battery power rating did not show these effects. Further research need to be done with dP/dt limitation in order to prevent this behavior.

The energy losses generated by the storage operation are moderate between 2-5 %, depending on the operation scheme. In a grid with even higher renewable energy generation the storage might prevent derating during peak times.

Generally it can be seen that energy storage can balance the time difference between photovoltaic energy generation and load behavior. Furthermore the p.u. voltage within the grid can be improved taking moderate energy losses into account.

ACKNOWLEDGMENT The Lower Saxony research network 'Smart Nord'

acknowledges the support of the Lower Saxony Ministry of Science and Culture through the 'Niedersächsisches Vorab' grant programme (grant ZN 2764/ZN 2896).

REFERENCES [1] V. Khadkikar, R. K. Varma and R. Seethapathy, “Grid Voltage

Regulation Utilizing Storage Batteries in PV Solar – Wind Plant based distributed Generation System,” IEEE Electrical Power & Energy Conference, 2009

[2] A. Guetif, M. Kurrat, J. Meins, F. Turki, „Supporting the low-voltage distribution network with supercapacitors,” VDE-Kongress 2012, Stuttgart

[3] C. V. Nayar, M. Ashari, and W. W. L. Keerthipala, “A Grid-Interactive Photovoltaic uninterruptible Power Supply System using Battery Storage and a back up Diesel Generator,” IEEE Transactions on Energy Conversion, Vol. 15, September 2000

[4] J.-H. Psola, W.-R. Canders, and M. Henke, “Technologies and Operational Concepts for Energy Storages,” EnviroInfo 2013, Hamburg

[5] J.-H. Psola, W.-R. Canders, and M. Henke, “Modeling of a Redox Flow Battery Storage for Grid Applications,” PCIM Asia 2013, Shanghai

[6] K. Strunz, et al., “Benchmark Systems for Network Integration of Renewable and Distributed Energy Resources” CIGRE Task Force C6.04.02, 2013.

[7] S. Mukhopadhyay, S. K. Soonee, R. Joshi and A. K. Rajput, “On the Progress of Renewable Energy Integration into Smart Grids in India,” IEEE Power and Energy Society General Meeting, 2012, S. 1-6

[8] M. Sonnenschein, et al., “Distributed ans self-organized coordination in Smart Grids,” VDE-Kongress 2012, Stuttgart

[9] J. Chahwan, C. Abbey, G. Joos, “VRB Modelling for the Study of Output Terminal Voltages, Internal Losses and Performance,” IEEE Canada Electrical Power Conference, 2007, pp. 387 – 392

[10] L. Barote, C. Marinescu, M. Georgescu, “VRB Modeling for Storage in Stand-Alone Wind Energy Systems,” IEEE Bucharest Power Tech Conference, 2009

[11] J.-P. Macary, et al., “Smart Power Applications and active influence of power quality in distribution networks with Energy Storage Solutions,” VDE-Kongress 2012, Stuttgart

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