Dssim

Analysis of smart residential Energy Management using
DSSim
Distribution System Simulator
Ioana Raluca GAFTON
May-July 2013, G2Elab , Grenoble INP

Smart
Grids
Intelligent Devices
Renewable
Sources
Solar Panels, Wind
Turbines
Expensive
Stockage
just for a Short
Time
Consumer`s
decisions
Energy
Quqlty, Efficiency,
Energy Control
Diminuated Bills
Scheduled Consum

International
circumstance
Evolutions and
global challenages in
energetical field
Integrating
New
Sources
Programing
and
Adjusting
At the client side
Distribution
System
Simulator

Tehnical
Programmable
Economical
Real time simulation
Local grid weakness and straightness
Intelligent devices
Scheduled consuming + Automatic start-up
Energy price + Electricity tarrif
Reduced bills - saving money

Tehnical
• Concering to all
tehnical constraints
and restraints of the
grid.
• Specificed and
individual
equipments, trought
the entire related
system.
• Incressing the stability
of the local grid.
• Integrating renewable
sources to the house, to
the local area.
Programmable
• Incorporating intelligent
devices (smart
meters, automatic plug-
in, control and
visualisation softwares)
which allow to start, to
reschedule or to close
an equipment
connected at electricity.
• Assistance services.
• Developing the bilateral
communication
between consumers and
energy delivers.
• In time and accesible
information.
Economical
• Specificed
advantages in
money terms.
• Personalized
options.

 Atfer all, DSSim should be seen like a capable, discret, strong, sensitive software
for electrical distribution systems and networks, thought to help the public
service, the society and, also, the consumers.
Public service
• Matching the traditional
sources with the
alternative ones,in
generation the
electricity
• Reducing the costs, by
automatic operation
• Security
• Quality
• Reliability
• Availability
Consumers
•Adaptable costumer`s
behavior by sophysticating
him
•Easy to transform data into
information
•Integrated the renewable
energy in distribution
•Reducing bills
•Increasing the comfort
Society
•Advanced distribution
automation (Volt-Var
control; fault control).
•Positive effect on the
environment
•A better abridgment at the
global energy consuming.

 Classified the sources to the house
House
Electrical
grid
Solar
panels
Electric
car

 Classified the equipments from the house
• Solar panels
Production
• Controllable
• Washing machine
• Dishwash machine
• Radiators (heating)
• Electric car
• Non-controllable
• Fridge
• Deepfrezer
• Daily NCE
Consuming
equipments

 Developing the house model in DSSim:
 Decoding the equipment as an unique node, associated with a switcher
 Creating the daily curves for each equipment (Load Profile)

 Tests and simulations based on the next scenario:
 Winter study case – February
 The EV is seen as an electricity consumer, not taking into consideration that it could generate
or supply the grid energy demand.
 All the equipments remain connected to the grid; but they can be unplugged through smart
devices (automatic draines).
 The controllable equipments had required the start time, in terms of maximal energy taken
by hour.
 The non-controllable ones are not disconnected; the fridge and the deepfrezer have their own
curve, but the rest of non-controllable equipments has the same loadshape of the daily
curve, with apex, peak and normal zones.
 The extra energy produced by solar panels will be stockage in the batteries of the
client, collected each month and used the next month for different equipments.

 Charging during the nights
-starting to 23:00-06:00 (0% - 95%)
-starting to 00:00-06:00 ( 10% - 95%)
-starting to 00:00-07:00 ( 0% - 95%)
 Discconeting between
-11:0-17:000
-11:00-13:00 and 14:00-17:00
- 23:00-06:00
 Starting after
-22:00
-00:00
 Dwm - after 5:00

 The PV load need to be synchronized to each apart month;The PV production need to be
conform to the solar panels dimensions, and them efficiency.
 Starting DWM and WM during the night or late in the morning directes to a increased
total energy demand from the grid;
Solution: Programming their star during the day , when there is energy from the solar panels, WM- 13:00, DWM-14:00
 The maximal power required to the grid does not be bigger than 3.68 kWh/h, otherwise all
the equipments will be blocked;
 Charging EV from 23:00 will affect the others non-controllable equipments;The same
situation for 00:00-07:00
Solution:Charging after 00:00, when non-controllable equipaments are turned off
 Using a 2000W and a 1200W radiators for heating induce to a big grid demand;
Solution:1* 2000W is replaced by 1*1200W; 2*1200W are replaced by 2*800W; they are turn on between : [(07:00-11:00), (14:00-
15:00) , (17:00-23:00)]

 We know the scenario
 We pick the right equipments
 We built the programme
 We had anterior simulations

Equipment Energy
Label(kW)
No Time of function
Radiator 1.2 1 (07:00-11:00),(14:00-15:00), (17:00-23:00)
Radiator 0.8 2 (07:00-11:00),(14:00-15:00), (17:00-23:00)
Wash Machine 2 1 (13:00- end)
Dishwash machine 2.2 1 (14:00- end)
Fridge 0.15 1 (00:00- 24:00)
Deepfrezer 0.35 1 (00:00- 24:00)
Non-controllable 1.8 1 (00:00- 24:00)
EV 3 1 (00:00-06:00)

0
0.2
0.4
0.6
1 3 5 7 9 11 13 15 17 19 21 23
Consuming[kWh/h]
Radiators Daily Curve
Radiator 1200W
Radiator 800W
0
0.05
0.1
0.15
0.2
0.25
0.3
1 3 5 7 9 11 13 15 17 19 21 23
COnsuming[kWh/h]
Permanent Plug In Equipaments Daily Curve
Fridge
Deepfrezer
0
0.5
1
1.5
2
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 192021222324
Cosnuming[kWh/h]
Non-controllable Equipaments Daily Curve
0
1
2
3
4
1 3 5 7 9 11 13 15 17 19 21 23
Consuming[kWh/h]
ElectricCar Daily Curve
0
0.5
1
1.5
1 3 5 7 9 11 13 15 17 19 21 23
Consuming[kwh/h]
Programable Equipaments
Dishwash Machine
Washing Machine
0
1
2
3
4
5
1 3 5 7 9 11 13 15 17 19 21 23
Production(kWh/h)
PV Daily Curve

-5
-4
-3
-2
-1
0
1
2
3
4
5
0 1 2 3 4 5 6 7
(kWh/h)
House behavior - one week
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0 1 2 3 4 5 6
(kWh/h)
PV production

 The next data are based on the previous observation

Equipment
Consuming
(kWh/month)
Radiator 1200 153.47
Radiator 800 102.36
Frigo 21.19
Deepfrezer 42.37
Washing machine 9.24
Dishwashing machine 16.89
EV 501.87
Non-controllable equipments 242.64
PV production 563.05
Grid demand
Stockage
Month
consum

 Analysis of different tarrif –study case applied for Roumania
1. Situation :Non-eligible consumer ( or domestic consumer, more than 90%
cases)
2. Situation: Eligible consumer
996.98
365.45
631.55

996.98
365.45
631,55
Characteristics:
• total energy demand
• energy consumed during
the day / night
• energy consumed at apex /
peak / basic hours
Tarrif Details Bill
value
SociallyTarrif Social:
E1*p1 + E2*p2 + (E-E1-E2)*p3
1098.08
Practically Tarrif Monom:
E*p
639.35
Profitability
Tarrif
Monom with included consuming:
pa*N + (Ed-1)*N*p
509.75
ComftyTarrif Monom with reservation:
E*p + N*pr
510.88
AlternatelyTarrif Monom differenciated in 2 zones:
DE*pd + NE*pn + N*pr
425.26
PlusAleternately
Tarrif
Monom differenciated in 3 zones:
AE*p_a + PE*p_p + NE*p_n + N*pr
700.36
OptimallyTarrif Monom based on contracted power:
E*p + N*pr
442.73

996.98
365.45
631,55
Characteristics:
• total energy demand
• energy consumed at apex
/ peak / basic hours
• energy consumed during
the day / night
• power consumed during
the apex / peak hours
• maximum contracted
power
Tarrif Details Bill value
A33
Tarrif
Binom, diferenciated in 2 zones and time of
maximal power usage
AE*p_AE + NE*p_NE + PE*p_PE +
(AP*p_AP*d)/365 + (PP-Ap)*p_PP*d/365
520.44
A Tarrif Diferenciated Binom:
AE*p_AE+ RE*p_RE+ AP*p_AP*d/365+ (RP-
AP)*p_RP*d/365
602.32
CTarrif Simple Binom:
E*p_E + max(MaxCPow,MaxMPow)*p_P*d/365
561.35
B Tarrif Diferenciated Monom:
AE*p_AE + RE*p_RE
526.69
DTarrif Simple Monom:
E*p_E
515.23
E1Tarrif Day-Night:
DE*p_DE + NwE* p_NwE
430.75
E2Tarrif Day-Night:
DE*p_DE + NE* p_NE
430.20

Non-eligible consumer Eligible consumer
• The medium power required by the
smart home is 35kWh/day, with a
maximam of 3.48kWh/h!
• The best two options are: Altervativelly
Tarrif and Optimally one, which include
an energy reservation from the grid;
• The most dezadvantage tarrif is the
Social one, being a 3 times bigger than
the first option;
• Differencing the consuming between
apex, peak, and basic hours detemines
an increasion of 85% of the bill;
• At this consuming, Profitability Tarrif
and ComftyTarrif are quiet familiar, but
remain a 30% an inflated option.
• The best options are : Day-NightTarrif (E1
and E2), case in which the daily consum is
a quarter from the total one;
• Even if the house has well definited the
forecast consum, using the binom
differencial tarrif is not the desired option;
Although the consum in the apex hours is
equal to 18% from the rest ones, on the
binom tarrifs the consumer need to pay
also for the power value.
• Would be better to used it a differenciated
monom (apex energy and rest energy) but
this also generates a 30% incresement.
• An energy tarrif is cheapper than an
energy & power tarrif option.
At this level, the owners of the smart house wouldn`t be paid, because we did not take into
consideration the possibility of deconnecting, or/and selling PV energy, or/and generating from EV.

 Testing different options for stockage energy (solar energy)
 Testing for A /A++ class equipment (frigde, deepfrezer)
 Using EV as a source, its battery as an energy generator
 Total power of consumers ≤ power of sources
 Making a two months analysis for different scenarios, to distribuite the
energy from PV
 Creating a summer scenario
 Replacing the radiators with conditioned air
 Adjusting the stockage for solar panels
 Implicating more the consumer

 http://freepdfdb.org/ppt/renewable-and-energy-efficiency-powerpoint-presentation-10862724.html
 http://freepdfdb.org/ppt/smart-grid-amp-integration-of-renewable-energy-resources-2036313.html
 http://freepdfdb.org/ppt/powerpoint-presentation-59605769.html
 Today reading, is not finished

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