1. First simulation results
Smart energy program
First simulation results
Luca Pilosu – MLW Area

2. Agenda - activities 2013
Identify a simulation tool suitable for the program’s goals
First implementations
PV panel
Loads
Batteries
Control logic
Next steps
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ISMB - Copyright 2013

3. State of the art of simulation tools
Analysis done together by MLW and PerT areas
Also based on previous deliverables
Considering licensing costs
Goal: choosing the most suitable simulation tool
Matlab + Simulink selected
Several works about smart grid simulation use these tools
Pre-built blocks for system components
Scalable simulation
Easy to try modifications/improvements
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4. PV panel Implemented using Matlab + Simulink
First implementation with approximate formula
INPUTS:
Solar radiation
Temperature read from XLS file
Family loads
OUTPUTS:
Produced power
Instantaneous difference between self-production and family load
Integrate the difference
Evaluate the theoretical self-sufficiency of a prosumer
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5. Implemented architecture
Formula:
Pac=Pn*G/1000*(1+γ*ΔTc)*0.92*ηdc-ac
Item
Value
Unit Pn
3000 W G
from "Daily Irradiation" sheet
W/m2 γ
-0,50%
%/°C
ΔTc
Tc-25 °C Tc
Tamb+(NOCT-20)*G/800
Tamb
from "Daily Temperature" sheet
NOCT 45
ηdc-ac
0,98
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ISMB - Copyright 2013

6. PV block: detailed description
Formula:
Pac=Pn*G/1000*(1+γ*ΔTc)*0.92*ηdc-ac
Item
Value
Unit Pn
3000 W G
from "Daily Irradiation" sheet
W/m2 γ
-0,50%
%/°C
ΔTc
Tc-25 °C Tc
Tamb+(NOCT-20)*G/800
Tamb
from "Daily Temperature" sheet
NOCT 45
ηdc-ac
0,98
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ISMB - Copyright 2013

7. Output (January)
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8. PV block: detailed description
Introduced integration for calculating self-production
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9. Self-production (January vs July)
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10. Weather station Raw data are made available from the weather station
It will be possible to exploit our own data to feed the PV panel model
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11. Introducing battery (1st approach)
First approach
‘Queue’ model
Definition of a ‘quantum’ of energy
Battery instantiated as an object
Properties
State of Charge
Methods
Charge
Discharge
Act only on the State of Charge (SOC)
Need for a more accurate model
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12. Introducing battery (2nd approach)
Fine modeling of the energy storage
Based on approaches described in literature
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13. Introducing battery (2nd approach)
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14. Battery model Lead-Acid model [1] Charge (i* > 0)
Discharge (i* < 0)
[1] Tremblay, O., Dessaint, L.-A. "Experimental Validation of a Battery Dynamic Model for EV Applications." World Electric Vehicle Journal. Vol. 3 - ISSN © 2009 AVERE, EVS24 Stavanger, Norway, May , 2009
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15. SuPER project Sustainable Power for Electrical Resources
‘Design and simulation of photovoltaic super system using Simulink’
California Polytechnic State University
Electrical Engineering department
Year 2006
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16. SuPER project
They use custom blocks (e.g. for batteries), defined from scratch
The architecture is similar to what we want to model
Adopted this scheme with specific Simulink blocks
Adaptations needed
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17. Introducing battery Load: constant Charger: controlled current source
Series RLC load (due to some compatibility problems between components of different blocksets)
Resistive load
Charger: controlled current source
Triggered by a relay
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18. 1 Battery with RLC load
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19. Adding a 2nd battery
For preliminary tests, control based on the State of Charge of 1st battery (SOC1)
When fully charged, put in ‘discharging’ mode
When under 30%, put in ‘charging’ mode
Battery n.2 experiences state transitions without driving them
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20. 2 Batteries with resistive load
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21. Refining the control logic
Actions driven by both SOC1 and SOC2
Swap
Buy
Sell
do nothing
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22. Refining the control logic
Create a flow chart for making decisions
To be further refined!
B1 charging
Start
SOC1=100%
SOC2<100% NO
YES
SELL
SWAP
DO NOTHING
B2 discharging
Start
SOC2≤30%
SOC1>30% NO
YES
BUY
SWAP
DO NOTHING
13/11/2018
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23. Battery states State of Charge can be ‘Full’, if equal to 100%
‘Partially charged’, if between 30% and 100%
‘Almost empty’, if below 30%
Also not to run outside the linear zone
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24. Table of truth - battery states
B1 SOC
B2 SOC
B1 state
Action + -
DO NOTHING
+/-
BUY
SWAP
SELL
State:
charging
discharging
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25. Table of truth – Simulink implementation
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26. Next steps Dynamic load Battery aging
Controlled resistor
In order to use real data with the model
Battery aging
Limit battery life in terms of charge/discharge cycles
Look for some references in literature
Put together the ‘ingredients’ prepared so far
PV panel
Loads
Battery
Control logic
Give metrics/prices to
Blocks
Actions
Start implementing some refined logic
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ISMB - Copyright 2013

27. ISMB - Copyright 2013