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Local Energy & Smart Appliances

Strategies for a increasing the use of local renewable en-ergies by smart appliances

WP 3 working paper from the Smart-A project

A report prepared as part of the EIE project„Smart Domestic Appliances in Sustainable Energy Systems

(Smart-A)”

D 3.1

Written byChristian Möllering ([email protected]), enervision GmbH

The project "Smart Domestic Appliances in Sustainable Energy Systems (Smart-A)" is supported by the European Commission through the IEE programme (contract no. EIE/06/185//SI2.447477).

The sole responsibility for the content of this report lies with the authors. It does not represent the opinion of the European Communities. The European Commission is not responsible for any use that may be made of the information contained therein.

© 2007

Smart-A D3.1 Local energy & Smart Appliances

Content

1 Introduction..............................................................................................................................6

1.1 The Smart-A project.......................................................................................................61.2 Definition of Local Energy Systems..............................................................................61.3 Communication..............................................................................................................71.4 Demand Side..................................................................................................................7

2 Renewable Inputs.....................................................................................................................8

2.1 Solar Energy...................................................................................................................82.2 Wind Energy...................................................................................................................82.3 Biomass Cogeneration....................................................................................................92.4 Regional Aspects..........................................................................................................10

3 Demand Side...........................................................................................................................12

3.1 Electricity.....................................................................................................................123.2 Heat...............................................................................................................................133.3 Cold..............................................................................................................................13

4 Strategies of Smart Appliances.............................................................................................14

4.1 Calculation of the Price Signal.....................................................................................144.2 Appliance Algorithms..................................................................................................14

4.2.1 Storage Based Appliances.............................................................................144.2.2 Non-Urgent Loads.........................................................................................164.2.3 Strategies for Cogeneration Plants...............................................................16

4.3 Control Strategies.........................................................................................................20

5 Simulation...............................................................................................................................21

5.1 Exemplary Set-Up........................................................................................................215.1.1 Climatic Data.................................................................................................215.1.2 Renewable Energies.......................................................................................225.1.3 Methodology .................................................................................................22

5.2 Parameter Studies.........................................................................................................225.2.1 Variation........................................................................................................225.2.2 Results............................................................................................................22

6 References...............................................................................................................................24

7 Annex......................................................................................................................................25

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Local energy & Smart Appliances Smart-A D3.1

List of figures

Figure 1 -1: Local energy systems (LES) embed households and are part of the supra-local electricity grid...........................................................................................................7

Figure 2 -2: Solar electricity potential in European Countries.........................................8

Figure 2 -3: European wind resources at 50 metres a.g.l.................................................9

Figure 2 -4: Share of renewable energies in primary energy consumption of European Union countries in 2005 (in %).......................................................................................10

Figure 2 -5: Share of renewable energies in gross electrical consumption in European Union countries in 2005 (in %).......................................................................................11

Figure 4 -6: Smart control scheme for storage based appliances...................................14

Figure 4 -7: Potential working hours per day in an average household, depending on the nominal power of the micro-CHP...................................................................................17

Figure 4 -8: Possible control strategy for allocating micro-CHP as compensation of fluctuating renewable energies........................................................................................20

Figure 7 -9: Three exemplary days in Hamburg with conventional control..................25

Figure 7 -10: Three exemplary days in Hamburg, Smart Control 0-0............................25

Figure 7 -11: Three exemplary days in Hamburg, Smart Control 0.1-0.1......................26

Figure 7 -12: Three exemplary days in Hamburg, Smart Control 0-0.2.........................26

Figure 7 -13: Three exemplary days in Trapani, conventional control...........................27

Figure 7 -14: Three exemplary days in Trapani, Smart Control 0-0...............................27

Figure 7 -15: Three exemplary days in Trapani, Smart Control 0.1-0.1.........................28

Figure 7 -16: Three exemplary days in Trapani, Smart Control 0-0.2............................28

List of tables

Table 3 -1 Service offer and energy need of different appliances...................................12

Table 5 -2 Weather Data of the US DOE Building Technology Program......................21

Table 5 -3 Hamburg yearly energy sums for different control strategies........................22

Table 5 -4 Trapani yearly energy sums for different control strategies..........................23

List of boxes

Box 4 -1 Calculation of the Price Signal.........................................................................14

Box 4 -2 Calculation of Status of Fridge.........................................................................15

Box 4 -3 Calculation of Operation of Fridge...................................................................16

4


Box 4 -4 Calculation of Status of Heating System..........................................................19

Box 4 -5 Calculation of Operation of Micro-CHP..........................................................19

Glossary

US DOE United States of America Department of Energy

WMO World Meteorological Organisation

ASHRAE American Society of Heating, Refrigerating, and Air-Conditioning Engineers

5


1 Introduction

1.1 The Smart-A projectMain objective of this report is to look at the possibilities of using more renewable en-ergies within local systems by smart control of household appliances. Therefore, besides a reference model without any smart activities the potential of smart control algorithms for different household appliances in different regional contexts are examined.

Additionally, technical implementations issues are reviewed. Being not planable, the in-tensive use of renewable energy sources affords more flexibility on the consumer side, a so called demand response (US DOE, 2006). One important aspect of a demand re-sponse is the fact that the appliances on the demand side need information. This will probably be a price signal, and in the report we start off there.

The local use of renewable leads to less necessity on supra-regional energy flow and generally attenuates the volatility of the electric grid just where the renewable energies are feed in. Last but not least it has the psychological effect of using one's “own” en-ergy.

1.2 Definition of Local Energy SystemsA local energy system is defined as a agglomeration of households and small enterprises within an area, where meteorological circumstances are constant and the distribution of heat is meaningful considering nowadays technology. According to electricity, only low and medium voltage levels are used at this scale.

6


Figure 1 -1: Local energy systems (LES) embed households and are part of the supra-local electricity grid.

1.3 CommunicationThe distribution of any price or similar signal is in fact a very decisive point. Therefore it will be covered in a separate report within this project. Herein we assume that any ne-cessary communication between all parts of the energy system is given.

1.4 Demand SideWe look at households in this first step, because we consider mass effects of many sim-ilar structured sub-systems and the effect of smart household appliances.

In this work package, besides an examination of renewable sources, an algorithmic ap-proach towards the suggested smart strategies is evolved, in order to be able to simulate their effect within a local energy system as above defined.

Thus, besides meteorological data, which is available in hourly values, the probabilities of the use of the different household appliances as well as their technical behaviour are integrated in numerical analysis.

As result, parameter studies of different relations between local renewable energy sources and the penetration of smart household appliances give information on import respectively export of electricity, and the shifting, energy saving and attenuation poten-tial of smart appliances on local level.

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SUPRA-LOCAL GRID

LES

HH

HHHH

HH

HHHH

HH

LES

HH

HHHHHH

HHHH

HH

LES

HH

HHHH

HH

HHHH

HH

LES

HH

HHHHHH

HHHH

HH


2 Renewable Inputs

2.1 Solar Energy

Figure 2 -2: Solar electricity potential in European Countries

Source: Huld 2007

The yearly global irradiation differs from around 1000 kWh/m²a in middle and northern Europe up to 2000 kWh/m²a in southern regions of Spain and Italy. Solar irradiation has a clear characteristic of having a maximum at noon and being zero at night as well as the seasonal variation. The influence of clouds makes it irregular, though.

Solar energy may be used by photovoltaics to produce electricity or by thermal collect-ors to generate heat.

2.2 Wind EnergyThe wind energy potential is very inhomogeneously distributed over Europe. Addition-ally, wind energy is strongly fluctuating in time.

8


Figure 2 -3: European wind resources at 50 metres a.g.l.

Source: Riso 1989

2.3 Biomass CogenerationIn opposite to the first two options, cogeneration plants fed by biomass have a steady operation. They are the base load plants of renewable energies. Nevertheless, they can react to a certain extent, similar as the smart appliances, to the current energy balance in the local energy system. They have to take into account the gas production and the heat demand as well.

9


2.4 Regional AspectsThe penetration of the energy system by renewable energies differs much over Europe (see Fig. 3). In the electricity sector the shares are again different (Fig. 4).

Figure 2 -4: Share of renewable energies in primary energy consumption of European Union countries in 2005 (in %)

Source: Eurobserver 2006

10


Figure 2 -5: Share of renewable energies in gross electrical consumption in European Union countries in 2005 (in %)

Source: Eurobserver 2006

11


3 Demand Side

3.1 ElectricityThe household as main element of the local energy system's demand side consists of several appliances. They each have a certain profile, which makes them more or less at-tractive for smart interaction to renewable energy sources.

Table 3 -1 Service offer and energy need of different appliances

Device Storage Ability

Shifting Potential

Electricity needed

Heat needed

Peak Time Service provided

Fridge + * + -- -- Cooled goods

Freezer + * + -- -- Frozen goods

Washing Machine

-- + + (30°-60°C) -- Cleaning of clothes

Tumble Dryer

-- + + (Gas) (> 100 °C) -- Drying of clothes

Dish Washer

-- + + (50°C) After meals

Cleaning of dishes

Boiler with tank

+ * + / Gas (50°C) Morning & evening

Hot water

Flow Heat-er

-- -- + / Gas (50°C) Morning & evening

Hot water

Cooker -- -- + / Gas (> 100 °C) Lunch / Diner

Cooking food

Room heating

+ * (+) / Gas / (30°-70°C) Morning Room tem-perature

Lighting -- -- + -- Morning & evening

Light

Computer -- -- + -- Morning to Midnight

Informa-tion

* because of storage ability

Source: C. Möllering

12


Besides the electricity demand the heat demand will be taken into account for the households. WP2 reports give information in much more detail.

3.2 HeatFor a LES as defined in chapter 1.2 it is assumed that the heat may be distributed all over the LES (District Heating). Therefore, heat demand of the LES is covered by a set of heat sources in the system, as there are

• Boilers

• Heat Pumps

• Cogeneration Plants

• Solar Thermal Collectors

Solar collectors supply heat if there is respective weather, boilers will be used as backup. Heat pumps produce heat out of electricity and may work as balancing device between heat and electricity demand. The control strategy for cogeneration plants between heat and electricity demand is complicated and multi-dimensional (see chapter 4.4).

3.3 ColdIn southern European countries the cold load in summer is severe, while there is still a heat load for hot water. In the first step this cold will be produced electrically, as it usu-ally happens. New technologies which may change this in future are not taken into ac-count.

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4 Strategies of Smart Appliances

4.1 Calculation of the Price SignalImportant for a practicable calculation of a price signal is the evident existence of the basic data. In the following set- ups the presumption always is that the local system has one interface, where energy import or export in respectively from the local system hap-pens. The balance can be calculated permanently and easily. In our model a price signal of 1 is balanced, below 1 additional energy is needed, above 1 there is locally more re-newable energy than is consumed.

Box 4 -1 Calculation of the Price Signal

4.2 Appliance Algorithms4.2.1 Storage Based Appliances

From the mathematical point of view, storage based appliances are similar from the al-gorithmic point of view but apply different parameters. All they are a storages, which are periodically powered up to compensate static losses via the insulation and dynamic ones due to door openings, unloading etc.

Figure 4 -6: Smart control scheme for storage based appliances


In figure 4-6 the yellow line gives the available renewable energies, the violet line the abstract status of the device, i.e. a refrigerator with high status has a low temperature. The conventional operation shines red, while the “smart” operation is green stated.

For smart control a two band algorithm applies. While the conventional control remains unchanged, a second, overlapping band is defined for smart control in case of renewable sources available. This is a fully automatic approach, which is most suitable for fridges, freezers, hot water and heating.

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1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23

0123456789

101112131415

Control of storage based appliances REStatusActualLossConv. OPRE OP

Hour of Day

Stat

us ConventionalControl

Smart Control

PS t =RE /Consumption

withPS t : Price signalRE : Currently available renewable energiesConsumption: Current consumption


Box 4 -2 Calculation of Status of Fridge

Box 4 -3 Calculation of Operation of Fridge

4.2.2 Non-Urgent Loads

Under this category you find washing machines, tumble dryers, dish washers, which have no storage characteristic and may be started and disconnected under certain cir-cumstances, which is not simply a temperature signal as in the storage case. These ap-pliances are much more difficult to mathematically describe are stay focus of future works, together with WP2.

15

T t =T t−1∗1Q iQ d∗N /m∗c

withT t : Current temperatureQi : Loss via insulationQd : Loss due to unloadingN : Unloadings per intervalm : Internal massc : Heat capacity

P c t = sRc t ∨Rc t ∧R st ∗Pc0

Rc t =T t ≤LL∨P c t−10∧sPc t =0

R st =! T t HL ∧RE t MinRE ∨T t ≥sHL

sRc t =T t sLL∨T t sHL∧ sPc t−10∧RE tMinRE

withP c t : Actual PowerRc t : Conventional control limited by smart controlR st : Smart off criteriasRc t : Smart controlLL : Low limit of controlHL: High limit of controlsHL: Smart high limitsLL : Smart low limitP c0 : Nominal PowerRE t : Current renewable energy levelMinRE : Minimal renewable energy available before smart actions start


4.2.3 Strategies for Cogeneration Plants

Usually cogeneration plants, especially of smaller size, are input (e.g. biogas) or heat demand driven. For stabilisation of the electric grid a modulation of the electricity pro-duction would be useful. Thus, incentives must be defined for providing a certain per-centage of the power of a cogeneration plant for stabilisation. Though, in most cases surplus heat can only be stored in heat storages and not dissipated by extra coolers.

In the documentation of his model EnergyPLAN (Lund 2007) Henrik Lund gives four optional strategies for local cogeneration plants:

1. Entirely heat driven (no coolers necessary).

2. Entirely electricity driven (requires coolers).

3. Using cogeneration plants for stabilising the electric grid while balancing the heat/electricity mismatch by heat pumps (still requires some coolers).

4. Like (3), but using large power plants for stabilising the grid in case the cogener-ation plants exceed their heat sink (no local coolers necessary).

But even a partly cooled local cogeneration plant has a better energy efficiency than a central power plant, where the entire heat production is dissipated.

The approach of smart cogeneration plants in parallel to smart household appliances fol-lows the idea of a cogeneration plant being “aware” of the heat status in its system and therefore dynamically contributing power for grid stabilisation.

Because of its inherent limits, micro-cogeneration in combination with a heat storage, which is standard, must in fact handled as storage based appliance. Though, the con-sumption characteristic, strongly varying over the day and the year, plays a much more important role than with e.g. a refrigerator.

The most crucial point of using micro-cogeneration plants in home environments is the yield which can be reached. As to see in the figure below, the variation of the size of the small cogeneration plant strongly changes the amount of working hours per day, assum-ing a daily heat storage in an average household.

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Figure 4 -7: Potential working hours per day in an average household, depending on the nominal power of the micro-CHP


While an economic operation under current frame conditions begin with minimal 3000-4000 working hours per year, the fit between local heat consumption and heat pro-duction by the micro-CHP must be quite well, for economic and for technical reason (cooler). Changing the economic boundary conditions by e.g. delivering balance power with a set of smart micro-CHP plants (which could give higher income than renewable energy bonuses), it could be even worth building coolers or small neighbourhood heat-ing networks.

In mathematical terms, the operation of a smart micro-CHP is similar to the fridge ex-plained in chapter 4.2.1. The difference is, that a micro-CHP plant is able to modulate between approximately 50 to 100 % of its nominal power. This is taken into account us-ing 2 different possible operation powers, a lower one for conventional operation, a higher one for special operation. In fact, the controlling price signal is the reciprocal of the available renewable energies.

Further more, the state of the heating system is calculated differently. Main change is the unloading. It is not driven by door openings, but by the hourly heat demand, de-pending on hot water and heating needs.

17

Jan Feb Mar Apr Mai Jun Jul Aug Sep Okt Nov Dez0

5

10

15

20

25

Working Hours Micro-CP

6 kWth10 kWth14 kWth

Hou

rs/D

ay


Box 4 -4 Calculation of Status of Heating System

Box 4 -5 Calculation of Operation of Micro-CHP

P c t = sRc t ∗Pc0∨Rc t ∧R st ∗Pc0∗PL

Rc t =T t ≤LL∨Pc t−10∧sPc t =0

R st =! T t HL ∧RE t MinRE ∨T t ≥sHL

sRc t =T t sLL∨T t sHL∧ sPc t−10∧RE tMinRE

withP c t : Actual PowerRc t : Conventional control limited by smart controlR st : Smart off criteriasRc t : Smart controlLL : Low limit of controlHL: High limit of controlsHL: Smart high limitsLL : Smart low limitP c0 : Nominal PowerPL: Part load factorRE t : Current renewable energy levelMinRE : Renewableenergy level below smart actions start

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T t =T t−1∗1Q iQ d/ m∗c

withT t : Current temperatureQi : Loss via insulationQd : Loss due to unloading of current hourm : Internal massc : Heat capacity


Figure 4 -8: Possible control strategy for allocating micro-CHP as compensation of fluctuating renewable energies


In Figure 4-8 the available renewable energies (RE) are given in yellow. The actual loss of the system is stated in blue. The status of the system, in case of a heat storage as in this example e.g. the current temperature has a violet colour. The standard operation is of the Micro-CHP is the red line (Conv. OP), while the smart strategy, compensating the fluctuation of the renewables (!RE OP), is given in green.

4.3 Control StrategiesIn the following we look a three variations of a indirect load control approach (see also WP4). It defines a unidirectional signal, on which the devices act individually. Thus, the central control does not know, which appliances currently take part. On the other hand, statistical effects could attenuate possible spike effects.

1. CC: Conventional control, no smart option

2. 0-0: Smart control in the same band as the conventional control

3. 0.1-0.1: Smart control has higher low limit and higher high limit than the con-ventional control

4. 0-0.2: Only the higher limit is higher than the conventional one, the lower limit is the same.

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1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23

0

10

20

30

40

50

60

Conventional & Smart Control Micro-CHP REStatusActualLossConv. OP!RE OP

Hour of Day

Stat

us Smart BoostConventional

Boost


5 Simulation

5.1 Exemplary Set-UpFor verification of the described algorithms a village district with 2000 households, one 500 kW wind turbine and 1000 m² photovoltaics. For simplicity, all other energy flows are not taken into account, only the behaviour of certain special control strategies of fridges versus the local renewable energy input. Two locations spread over Europe and covered by reference data are examined as example in a first flash: Hamburg, Germany and Trapani, Italy. Cogeneration is not yet included.

Any effects of the electric grid are not calculated. In the local context they are assumed to be irrelevant, the supra-local effects will be considered in WP4.

5.1.1 Climatic Data

Table 5 -2 Weather Data of the US DOE Building Technology Program

Country # of Locations Country # of LocationsAustria 5 Hungary 2Belgium 3 Ireland 7Bulgaria 3 Italy 63Cyprus 1 Lithuania 1Czech Republic 2 Netherlands 3Germany 9 Poland 4Denmark 1 Portugal 8Spain 53 Romania 6Finland 2 Slovakia 2France 12 Slovenia 1United Kingdom 10 Sweden 5Greece 3 SUM 206

Source: US DOE 2007

Besides European Test Reference Year data the U.S. Department of Energy (DOE) holds hourly data of more than 1300 locations worldwide. The European (WMO Region 6) data is available for several locations (see Table -2).

The sources of the weather data are the IWEC (International Weather for Energy Calcu-lations, result of the ASHRAE Project 1015), the Italian Climatic data collection "Gi-anni De Giorgio" (IGDG), INETI (Synthetic data for Portugal) and the Spanish Weather for Energy Calculations (SWEC).

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5.1.2 Renewable Energies

For the calculation of the renewable energy inputs rough methods were used in the local energy system simulation. For photovoltaics a conversion efficiency of 12 % is as-sumed. Wind energy input is calculated by a characteristic of a typical 500 kW wind turbine (Gerdes 1993).

5.1.3 Methodology

In preparation of simulations in larger models first studies were made on a smaller scale. For studying systems of a great number of parts with similar but not identical properties, e.g. in physics, stochastic methods of simulation like Monte-Carlo-Simula-tions are widely used (MacKeown 1997). To attenuate the behaviour of the appliances of some thousand households these methods are used for defining parameters, starting conditions and probabilities.

Additionally, the door opening characteristic by Thomas (Thomas, 2007) is implemen-ted into the in other aspects constant behaviour of fridges.

5.2 Parameter Studies5.2.1 Variation

Studies were undertaken over three different locations and each three different control strategies. Besides the yearly sums and averages three typical days (120-122 of the year) are shown in detail in the annex.

5.2.2 Results

The best strategy has the lowest balance, because a low consumption is combined with a high export (which is negative as flow out of the system).

It can be shown that a excessive use of local energy sources as with Smart Control strategy 0-0.2 leads to strong shifting towards local sources, but to significant overall losses.

A Smart approach within the same band as the usual control allows very small shiftings but more or less savings in the overall context. In one case, this effect is even of high order.

Table 5 -3 Hamburg yearly energy sums for different control strategies

Hamburg CC[kWh]

0-0[kWh]

0.1-0.1[kWh]

0-0.2[kWh]

Total Consumption 337899 334496 -1,0% 353389 4,6% 357059 5,7%

Import 93396 92291 -1,2% 91463 -2,1% 87440 -6,4%

Export 785228 787526 0,3% 767805 -2,2% 760112 -3,2%

Balance Im/Export -691832 -695235 0,5% -676342 -2,2% -672672 -2,8%


21


As criteria for the quality of the strategy the balance of import and export is introduced. The export of energy out of the local system is given physically correct negative, so the lower it is, the most efficient the local system behaves.

In the Hamburg case the strategy with smart approach on the same band as the conven-tional control seems to be the most successful one. Both strategies with lifted bands fail for their higher consumption. Though, the import of energy is most reduced by the 0-0.2 strategy.

Table 5 -4 Trapani yearly energy sums for different control strategies

Trapani CC[kWh]

0-0[kWh]

0.1-0.1[kWh]

0-0.2[kWh]

Total Consumption 339773 333465 -1,9% 355594 4,7% 356484 4,9%

Import 99129 96350 -2,8% 91509 -7,7% 88354 -10,9%

Export 1116498 1120027 0,3% 1093057 -2,1% 1089012 -2,5%

Balance Im/Export -1017369 -1023677 0,6% -1001548 -1,6% -1000658 -1,6%


The import reduction is maximal in the Trapani situation. The improvement on the im-port/export side is very small, though.

22


6 References

Eurobserver 2006 EurObserv'ER, State of renewable energies in Europe, 2006

Gerdes 1993 Gerdes, Gerhard J., Pahlke, Thomas, “Wind and Area Potential Ana-lysis of the Lower Saxonian Coast”, DEWI, Wilhelmshaven, 1993

Huld 2007 Huld, Thomas, Suri, Marcel, PVGIS © European Communities, 2001-2007

Lund 2007 Lund, Hendrik, “EnergyPLAN, Advanced Energy Systems Analysis Computer Model”, Aalborg, 2007

MacKeown 1997 MacKeown, P. Kevon, “Stochastic simulation in physics”, Springer, Singapore, 1997

Riso 1989 Risø National Laboratory, European Wind Atlas, 1989, Roskilde, Denmark.

Thomas 2007 Thomas, Sabine, Universität Bonn, personal message

US DOE 2006 US DOE Report to the Congress, “Benefits of demand response in electricity markets and recommendations for achieving them”, Feb-ruary 2006

US DOE 2007 US DOE, Building Technologies Program, EnergyPlus Weather Data, 2007

23


7 Annex

24

Figure 7 -9: Three exemplary days in Hamburg with conventional control

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

50

100

150

200

250

300

350

400

450

HAMBURG - Conventional

Sum REConsumption

hour of day

[kW

]

Figure 7 -10: Three exemplary days in Hamburg, Smart Control 0-0

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

50

100

150

200

250

300

350

400

450

HAMBURG - Smart Control

Sum REConsumption

hour of day

[kW

]


25

Figure 7 -11: Three exemplary days in Hamburg, Smart Control 0.1-0.1

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

50

100

150

200

250

300

350

400

450


Sum REConsumption

hour of day

[kW

]

Figure 7 -12: Three exemplary days in Hamburg, Smart Control 0-0.2

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

50

100

150

200

250

300

350

400

450


Sum REConsumption

hour of day

[kW

]


26

Figure 7 -13: Three exemplary days in Trapani, conventional control

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

100

200

300

400

500

600

TRAPANI - Conventional

Sum REConsumption

hour of day

[kW

]

Figure 7 -14: Three exemplary days in Trapani, Smart Control 0-0

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

100

200

300

400

500

600

TRAPANI - Smart Control

Sum REConsumption

hour of day

[kW

]


27

Figure 7 -15: Three exemplary days in Trapani, Smart Control 0.1-0.1

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

100

200

300

400

500

600


Sum REConsumption

hour of day

[kW

]

Figure 7 -16: Three exemplary days in Trapani, Smart Control 0-0.2

0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21 0 3 6 9 12 15 18 21

0

100

200

300

400

500

600


Sum REConsumption

hour of day

[kW

]

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