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Review of BS and AP management schemes

Łukasz Budzisz, Fatemeh Ganji, Adam Wolisz TU Berlin, Germany Gianluca Rizzo, IMDEA, Spain (currently with Univ. of App. Sciences of Western Switzerland) Marco Ajmone Marsan, Michela Meo, Yi Zhang, Politecnico di Torino, Italy Alberto Conte, Ivaylo Haratcherev, Alcatel-Lucent Bell Labs, France George Koutitas, Leandros Tassiulas, University of Thessaly, Greece Mario Pickavet Bart Lannoo, Sofie Lambert, Ghent Univ. - iMinds, Belgium

Energy efficient wireless access networks

n  Design approach for wireless access networks: q  Obtain maximum performance at full load

n  In reality, however: q  most of time, the wireless access networks are under low or

medium traffic load q  load profile exhibits large variation (close to the users) q  BS consumption is little load proportional and consume about

the same at any load

n  BS sleep modes are needed n  Sleep modes require BS management algorithms

Michela Meo – Politecnico di Torino

Savings between 20-40% can be

achieved

BS/AP management schemes - taxonomy


Proposed framework points out: n  the most important design aspects n  shortcomings and advantages n  energy-saving potential

n  Flat Network: with single type of BSs, macrocells/microcells only, one operator q  Non-overlapping architecture q  Overlapping architecture

n  Multi-Tier Network: with multiple types of BSs, also multiple technologies q  Wi-Fi offloading q  Femtocells

n  Mobile operator co-operation

Existing BS management schemes


Flat network with non-overlapping architecture


n  If the no. of BS always on is large, a slight increase of the RF output power is enough (if any)

n  If the no. of BS always on is small, RF output power increase are needed, as well as titling adjustments

Flat network with non-overlapping architecture


Two control schemes: n  Centralized approach à higher complexity of the decision,

higher guarantee of reliable QoS n  Distributed approach à BSs or groups of BSs takes

decisions independently

n  Micro stations are used to provide the required capacity under the coverage umbrella of macro stations

n  Two types of BSs: q  Critical stations: usually the macrocell, which cannot be put into sleep

mode, due to coverage issues q  Flexible stations: the BS that can be set in sleep mode

n  No need to increase the cell ranges or the parameters of the BSs remaining on (low probability of coverage holes)

n  Control schemes: n  Distributed approach: each flexible BS decides on the state of its operation

(on/off), independently n  Pseudo-distributed approach: Flexible stations are assigned to critical

stations, usually under cell overlap criteria

Flat network with overlapping architecture

Source: S. Kokkinogenis and G. Koutitas, “Dynamic and static base station management schemes for cellular networks,” in IEEE Global Communications Conference (GlobeComm ’12), Dec. 2012.


n  Marco-micro network co-exists and cooperates with other technologies, e.g. femto cell and WiFi

n  The main objective is to provide an offloading solution n  With WiFi:

n  Mobile operators usually do not have the administrative rights to the Wi-Fi APs n  Multi-Radio Access Technology (multi-RAT) is needed

n  Limitations and constraints for integration with existing BS system q  Guarantee that coverage holes do not occur (especially indoor scenarios) q  Software and hardware limitations of real equipment (availability of low

power states, transient times)

Multi-tier network


Sources: •  S. Kokkinogenis and G. Koutitas, “Dynamic and static base station management schemes for cellular networks,” in

IEEE Global Communications Conference (GlobeComm ’12), Dec. 2012. •  I. Haratcherev and A. Conte, “Practical energy-saving in 3g femtocells,” in IEEE Green Broadband Access (GBA)

workshop, in conjunction with ICC 2013, Jun. 2013. •  I. Haratcherev, M. Fiorito, and C. Balageas, “Low-power sleep mode and out-of-band wake-up for indoor access

points,” in GLOBECOM Workshops, 2009 IEEE, 2009, pp. 1–6.

Network sharing

load

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wor

k po

wer

load

BS

pow

er n  The low load proportionality of the devices makes the

whole access network little load proportional


P = Pconst + f (ρ)

Service provisioning cost is multiplied by the no. of networks

n  Several competing mobile operators cover the same area with their equipment

n  During low traffic periods, when the resources of one (or a few) operator are sufficient to carry all the traffic, make the operator share their infrastructure q  Switch off the network of one operator q  Let users roam to other operators

n  In the short term, inter-operator switching schemes can reduce the waste

n  In the long term, a unique efficient infrastructure with multiple virtual operators can be envisioned

Network sharing


Case study: Some European Countries

Country MNOs Market share [%] Subscr. [M]France 3 46 36 19 - 58.2

Germany 4 32 31 21 16 113.6Greece 3 51 28 21 - 15.4Italy 3 38 36 26 - 84.0

Netherlands 3 46 26 28 - 19.0Poland 4 29 29 28 14 47.5

Portugal 3 45 40 15 - 16.4Spain 3 44 34 22 - 51.4

Romania 3 41 32 26 - 24.2Russia 3 37 33 30 - 189.7

Ukraine 3 48 37 15 - 52.3U.K. 3 39 33 28 - 68.5

Table 1: Characteristics of the considered countries: Numberof MNOs offering both 2G and 3G services, market share foreach of the MNOs, total number of subscribers.

3. ENERGY BENEFITS IN EUROPEIn this section, we assess the effectiveness of network sharing interms of achievable energy saving by considering a number of Eu-ropean countries. In particular, we focus on the 12 countries indi-cated in Table ??, which are the countries whose total number ofsubscribers is larger than 15 Millions, according to publicly avail-able data.

For each country we collect approximate data about the number ofsubscribers for each of the active MNOs and the kind of providedservices. We then assume that network sharing is applicable onlyamong the MNOs that offer both 2G and 3G services. Indeed, aMNO offering access to 2G terminals cannot switch off its networkand make the users roam to a purely 3G network. In this case theoperator would probably switch off the 3G network leaving the 2Gaccess network on; however, since we only have aggregated dataabout the total number of subscribers and not the breakdown withrespect to the technology, we make the simplistic assumption thatnetwork sharing is implemented only among MNOs offering ser-vices to both 2G and 3G users.

Interestingly, the considered European countries present quite sim-ilar scenarios. As summarized in Table ??, except for two cases,namely Germany and Poland, all considered countries have 3 MNOsoffering both 2G and 3G services with relatively fair share of mar-ket. The smallest of the 3 MNOs has a share that is usually between20 and 30%, only in the case of Ukraine and Portugal the smallestof the three operators accounts for as low as 15% of the subscribers.Conversely, the largest of the 3 MNOs exceeds 50% of the marketshare only in Greece, where it is about 51%; otherwise, it is be-tween 37% and 48%. The case of Germany, with 4 MNOs, is in-teresting because it presents two dominant operators with about thesame number of subscribers, 36 millions, corresponding to 31% ofthe market, and other two smaller MNOs that share the remainingmarket. In Poland, three operators are about the same size, withalmost 30% of the share each, while the fourth operator accountsfor 14% of the market only. This substantial similarity of the situa-tions is probably due to historical reasons: in most of the Europeancountries similar network evolutions occurred roughly at the sametime.

We compute the energy saving achievable through network sharingfor each of the selected countries, and for both the consumer and

Figure 3: Saving achievable with network sharing in the Euro-pean countries with more than 15M subscribers; business andconsumer profiles, constant and variable cost models.

Figure 4: Utilization achievable with network sharing in theEuropean countries with more than 15M subscribers; businessand consumer profiles, week-days and week-ends.

the business traffic profiles shown in Fig. ?? (implicitly assumingthat the traffic profiles in Fig. ?? can be representative of trafficin all considered countries). Given a traffic profile and a country,we consider all the possible switch-off patterns, i.e., all the possi-ble orderings in which the MNOs of that country might switch off.Savings are obtained as described in the previous section, by de-riving switch-off and switch-on instants from (??), and by comput-ing saving from (??). Both the cases of variable and constant costmodels are evaluated. The saving achievable during week-days andweek-ends are properly weighted to get the average weekly saving.

Fig. ?? reports the maximum achievable energy saving, amongthose obtained from different switch-off patterns in a given sce-nario. The savings are really significant, typically larger than 40%:this confirms that network sharing, besides being a viable approach,already feasible with today technology, is very promising in termsof energy consumption reduction.

Observe also from the figure that the business traffic profile leadsto the largest saving. This is due to the profile having particu-larly steep transitions between peak and off-peak, and long peri-ods of very low traffic. Clearly, in reality, large service areas arecharacterized by a mixture of neighborhoods, some mainly withbusiness-like behavior of the users and others with consumer-liketraffic profiles. A switch-off scheme should then be applied byadapting, neighborhood by neighborhood, switching times to thespecific profiles. For example, a MNO that is going to switch-offits access network, might probably start from portions of the net-work in business areas, as soon as traffic drops below some thresh-old; some time later, when traffic drops also in the consumer ar-eas, other portions of the access network would be powered off. Interms of saving, this means that the achievable saving will be in be-tween what can be obtained from a business area and a consumerarea, with actual values depending on the traffic profiles and on theproportions of areas with business-like or consumer-like behavior.In case of some spare capacity, deployed to absorb medium termtraffic growth, some additional saving can be expected. With anoverprovisioning factor 1 + x = 1.2, for example, it is possible toreach savings between 50 and 59% for the consumer profile and be-tween 53 and 63% for the business profile under the constant costmodel. These values are even closer to the maximum theoreticalsaving that would be achieved when one network only has enoughcapacity to carry all the traffic; the maximum theoretical saving isequal to 66% for 3 MNOs, corresponsing to 1 network over threethat is carrying traffic, and it is equal to 75% for 4 MNOs.

A positive side-effect of network sharing is that active resources aremore effectively used than in traditional scenarios without sharing.Indeed, network sharing aims at reducing energy wastage that de-rives from daily periods of over-provisioning by making the avail-able capacity more closely follow the traffic profile. To evaluatethis effect, we compute the daily average utilization of the accessnetwork resources, by dividing the amount of generated traffic by



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consumer, constconsumer, var

business, constbusiness, var

Large savings, more than

30%!


Source: M. A. Marsan, M. Meo, “Network Sharing and its Energy Benefits: a Study of European Mobile Network Operators”, IEEE Globecom 2013 - Symposium on Selected Areas in Communications, December 2013.


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The key point is increasing

utilization


n  Game-theory based model q  Two MNOs cover the same area and offer the same QoS, but may have

different network planning strategies q  Heterogeneous architecture is assumed (different BS types) q  Deployment targets peak of the user demand q  Binary transitions: all traffic is migrated from one to the other MNO

n  The following game (transferrable utilities coalitional game of BS sharing) has been constructed: q  MNO 1 has different strategies corresponding to the chosen roaming price q  MNO 2 has the choice between roaming or no roaming its traffic q  Game objectives: reduce own costs by cooperation, receive fair share of benefits

of total cost reduction

Mobile operator co-operation

On-going work (iMinds, PoliTO, UTH, TUB): conference article “Greening the AirWaves: energy-efficient BS sharing” under preparation.


n  Power BSs with Renewable Energy Sources q  Deploy cellular networks in countries where the power grid does

not exist or is unreliable q  Achieve extremely low carbon footprints q  Survive natural disasters which damage the power grid

Possible scenarios: 1.  New opportunities for the deployments of networks in emerging

regions


From energy efficient networking to sustainable networking

Possible scenarios


n  Power BSs with Renewable Energy Sources q  Deploy cellular networks in countries where the power grid does

not exist or is unreliable q  Achieve extremely low carbon footprints q  Survive natural disasters which damage the power grid

Possible scenarios: 1.  New opportunities for the deployments of networks in emerging

regions 2.  New business models (high electricity price, green incentives and

sensitivity)


From energy efficient networking to sustainable networking

Dimension PV powering for a BS

n  Consider a typical BS q  Consumption profile q  Traffic profile

n  Choose a location n  Simulate energy production n  Simulate battery charge

and discharge to decide system dimensioning

18

energy need


BS consumption

•  When needed (no TLC infrastructure) wireless backhauling consumes

additional 200-250W, for a total of 30KWh/day

load

780

1350

0 1

Power [W]

Psleep, 450

load

500

840

0 1

Power [W]

Psleep, 336

Deep sleep Deep sleep

Macro cell with LTE technology, with and without Remote Radio Unit (PA close to antenna)


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n  Fast transitions n  Peaks during the day n  Large difference weekday/weekend

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Parameter L

Savin

g [%

]

Upper BoundDouble Switch Off (Max)1/2−2/31/2−3/42/3−3/43/4−8/9

Fig. 4. Double switch-off: network saving versus the parameter L for the synthetic traffic profile.

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WeekdayWeekend

Fig. 5. Business cell: weekday and weekend traffic profiles.

TABLE ICASE STUDY: SAVINGS WITH DIFFERENT SWITCH-OFF SCHEMES

Switch-off scheme S[%] - Business weekday S[%] - Consumer weekday

Single (8/9) 44.2 49.3Double (5/9)-(8/9) 47.5 51.0Triple (3/9)-(5/9)-(8/9) 48.7 51.3Maximum (Least-Loaded) 50.0 52.0

Traffic profiles: Business area

WD [KWh]

WE [KWh]

With RRU 15.2 12.55 Without RRU 23.9 19.5

Average Daily Consumption


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Fig. 6. Consumer cell: weekday and weekend traffic profiles.

11:00 15:00 19:00 23:00 03:0011:00

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Fig. 7. Business cell - weekday profile: network saving with double switch-off.

n  Slow transitions n  Peaks in the evening n  Little difference weekday/weekend

Traffic profiles: Consumer

WD [KWh]

WE [KWh]

With RRU 15.5 15.7 Without RRU 23.8 24.7

Average Daily Consumption


Three locations

!

H`!!

!Figura 28: Radiazione solare annuale in Europa (Fonte: PVGIS, Institute for Energy - Unione Europea,

2012)

!Figura 29: Radiazione solare annuale in Africa (Fonte: PVGIS, Institute for Energy - Unione Europea, 2012)

!

Torino (4KWh/m2, high

seasonal variance)

Palermo (5.1KWh/m2)

Assuan (6.8KWh/m2, low

seasonal variance)

!

H`!!

!Figura 28: Radiazione solare annuale in Europa (Fonte: PVGIS, Institute for Energy - Unione Europea,

2012)

!Figura 29: Radiazione solare annuale in Africa (Fonte: PVGIS, Institute for Energy - Unione Europea, 2012)

!

Solar radiation

Solar radiation


Dimensioning the system

n  Dimensioning based on the average of the days of the worst month: look for the system dimension that guarantees min. surplus

!

[H!!

!

Figura 31: Produzione mensile di energia elettrica (Torino)

!

Figura 32 Produzione mensile di energia elettrica (Palermo)

Hour

Pow

er [W

]

!

[X!!

!

Figura 34: Potenza nel giorno di riferimento per Torino

!

Figura 35 : Potenza nel giorno di riferimento per Palermo

Location w/o RRU KWp

w RRU KWp

Torino 20 14 Palermo 16 10 Assuan 8 6


Storing energy: Battery dimensioning

!

_X!!

!

Figura 43 Stato di carica delle batterie nell’arco di un anno –Torino – Impianto da 20 kWp

!

Figura 44 Stato di carica delle batterie nel mese peggiore –Torino – Impianto da 20 kWp

Torino

week

Bat

tery

cha

rge

[%]

Energy waste

Low charge (spoils

batteries)


Dimensioning the system: Panel area & batteries

Without RRU With RRU Location KWp Area

[m2] No. batt.

KWp Area [m2]

No. batt

Torino 20 98 75 14 68 45 Palermo 16 78 50 10 49 32 Aswan 8 39 30 6 29 16

n  Dimension battery so that charge never goes below 30% during the whole year

Results strongly depend on the location


Source: Marco Ajmone Marsan, Giuseppina Bucalo, Alfonso Di Caro, Michela Meo, Yi Zhang, Towards Zero Grid Electricity Networking: Powering BSs with Renewable Energy Sources”, IEEE ICC'13 - Workshop on Green Broadband access: energy efficient wireless and wired network solutions, June 2013.

Dimensioning the system: Panel area & batteries

Without RRU With RRU Location KWp Area

[m2] No. batt.

KWp Area [m2]

No. batt

Torino 20 98 75 14 68 45 Palermo 16 78 50 10 49 32 Aswan 8 39 30 6 29 16

n  Dimension battery so that charge never goes below 30% during the whole year

Very large! EE technology is needed


Combining RES with sleep modes

n  When traffic is below 50%, make 50% of the BS go to sleep mode

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Peak hours Off-peak hours


Use of sleep modes

n  While dimensioning of BS always on is basically unchanged, the dimensioning for BS going to sleep mode is much smaller

No sleep With sleep Deep sleep Location Area

[m2] Batt. Area

[m2] Batt. Area

[m2] Batt.

Torino 98 75 78 52 39 27 Palermo 78 50 58 27 29 17 Assuan 39 30 29 18 14 13

Significant improvement!

What about cost?

Area type

BS ISD [m]

Coverage [km2]

Grid only [K€/km2]

RES only [K€/km2]

Dense Urban 500 0.65 39.4 29.5

Urban 1000 2.60 9.8 7.4

Suburban 1732 7.79 3.3 2.5

Rural 4330 48.71 0.5 0.4

RES is cost effective!

Total cost: CAPEX+OPEX in 20 years


Hybrid powering: RES & grid

RES is cost effective!

No RES

Only RES

Hybrid

Total cost: CAPEX+OPEX


n  Higher degree of load proportionality is needed q  To adapt to traffic variations

n  Load proportionality can be partially achieved through q  BS sleep modes (intra-operator approach) q  Network sharing (inter-operator approach)

n  New promising scenarios with RES powering q  Sustainability q  Cost reduction q  But needs also EE solutions

Conclusions


Thank you!


Additional slides


Mobile traffic explosion vs energy costs n  Information and Communication

Technology (ICT) sector is responsible for 2% of global CO2 emissions and consumes 2-10% of global energy

n  Mobile traffic explosion will further aggravate this picture: q  increase of BS/AP density to fulfill the

demand

n  Up to 70% of cellular operators OPEX are energy costs

Breakdown of energy consumption (one of the European mobile network operators) Source: S. Vadgama and M. Hunukumbure, Trends in Green Wireless Access Networks, 2011

Radio access networks – prime target for energy saving


Components of a Base Station (BS)

Source: EARTH (Energy Aware Radio and network technologies) project, “Energy efficiency analysis of the reference systems, areas of improvements and target breakdown,” 2012.

n  Fixed power: cooling, main supply, DC-DC n  Load-proportional power: PA, BBU, RF


n  LTE is energy efficient: In urban areas, with a typical user density of 300 users/km2 q  LTE à 18W/user q  WiMAX à 27W/user q  HSPA à 68Wer/user

n  With the same transmission power, an LTE macro BS covers q  Urban area à about 0.22 km2

q  Suburban/rural environments à 2.6 km2

n  An LTE macro BS consumes around 1 kW q  Urban area à 4500W/km2 q  Suburban/rural environments à 400W/km2

Base station power consumption

Source: W. Vereecken, W. Van Heddeghem, M. Deruyck, B. Puype, B. Lannoo, W. Joseph, D. Colle, L. Martens, and P. Demeester, “Power consumption in telecommunication networks: overview and reduction strategies,” IEEE Communications Magazine, vol. 49, no. 6, pp. 62–69, 2011.


Load proportionality and power saving When k-1 BSs out of k can be in sleep mode (ρ<1/k), the power per BS becomes:

n  Sleep modes need to be introduced

n  Need for BS management algorithms

Normalized power consumption vs. load for different device types: n  10% proportion: the devices today n  50% proportion: the devices under

development n  90% proportion: not realistic with

today technologies

0.1

1

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0.001 0.01 0.1 1

Norm

aliz

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onsu

mpti

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BS load, !

10% propor.

50% propor.

90% propor.

with sleepno sleep

PS (ρ) =a+ kbρk

+k −1( )Psleep

k


n  Main obstacles to network sharing: n  MNOs are reluctant to allow their subscribers to frequently roam to a

competitor MNOs n  QoS provisioning in the visited network (dominant vs small MNOs) n  Need for extended roaming and billing procedures

Mobile operator co-operation


ZEN: an example

fully integrated photovoltaïc solution

optimized and evolutive solution

the project brings local skills

Objectives sustainable rural Telecom network greener solutions enabling Telecom development in bad quality

electric grid or off grid areas,

Social responsability enhanced telecom and digital inclusion with low cost solution positive impact on local economy (guards, maintenance jobs) solar electricity in excess freely provided for local use of populations

(cellular phone recharge, health centre powering, school lighting)

the project was initiated in Africa and is now deployedin 18 countries of France Telecom-Orange

Orange green strategy for AMEA zoneProject : optimized power consumption and solar powered mobile

networkMorocco

Egypt

Jordan

KenyaCameroon

Central African RepublicMadagascar

Niger

Mali

SenegalGuinea BissauGuineaIvory Coast

Equatorial Guinea

+ Dominican Republic,Vanuatu, Armenia, France

Morocco

Egypt

Jordan

KenyaCameroon

Central African RepublicMadagascar

Niger

Mali

SenegalGuinea BissauGuineaIvory Coast

Equatorial Guinea

+ Dominican Republic,Vanuatu, Armenia, France

Main results 2065 integrated solar Radio Access Network sites covering 3.3

million people 13 GWh solar energy produced in 2011 25 million liters fuel and 67000 tons CO2 saved in 2011 quality of service much better than for diesel generators

Innovation strong reduction of power consumption by

selecting high efficiency telecom equipment, no active cooling smart power architecture and management

optimized solar energy solution and less use of Diesel engines sizing and techno-economic tools

© Copyright France Télécom 2012

•  2065 access network sites, for 3.3 M people •  13GWh solar energy produced in 2011 •  25 Mliters fuel 67 Ktons CO2 saved in 2011


Dimensioning the PV system

!

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!


!


month

Av. d

aily

pro

duct

ion

[KW

h]

Torino •  Larger systems allow for larger production

•  Production changes according to seasons, while BS traffic and consumption not

n  A system is defined in terms of KWpeak: the achieved production in KW when radiation is 1KW/m2


Battery dimensioning !

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!


!


!

[[!!

!

Figura 33 Produzione mensile di energia elettrica (Assuan)

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+F&..*@!&!9*4(.*! +F&.2&13.)*!23++&!504E&!2(!'4*206(*.3!?!2('3.23.)3!1*+)*!2&++3!/)&;(*.(-!/(!

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)*)&+13.)3! .3(! 13/(! 3/)(E(@! &! 2(88343.6&! 2(! B03++(! (.E34.&+(! 2*E3! /(! *//34E&! 0.! 1*23/)*!

&013.)*!2(! '4*206(*.3C!Z!2(531A43@! &2! 3/31'(*@! *;.(! (.54313.)*!2(! #! dn!&++&! 2(13./(*.3!

23++F(1'(&.)*!5*1'*4)&!0.!&013.)*!2(!3.34;(&!'4*2*))&!2(!/*+(!`X@$!dn:C!M)3//*!2(/5*4/*!E&+3!

'34!P&+341*@!2*E3@! (+!/0223))*! (.54313.)*!2(!2(13./(*.(!23++F(1'(&.)*@!'4*2053!0.!&013.)*!

2(!'4*206(*.3!'&4(!&!"#X!dn:C!

M()0&6(*.3!5*1'+3)&13.)3!2(E34/&!&2!Z//0&.!2*E3@!/31'43!&!2(531A43@!&013.)&.2*!+&!

'*)3.6&!2(!'(55*!2(!#!dn!/(!:&!0.&!'4*206(*.3!1&;;(*43!2(!A3.!#[X!dn:C!!

!

!

month

Av. d

aily

pro

duct

ion

[KW

h]

Torino

Assuan

To absorb variability more batteries are

needed


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