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

Agenda - activities 2013 Identify a simulation tool suitable for the program’s goals First implementations PV panel Loads Batteries Control logic Next steps 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

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   13/11/2018 ISMB - Copyright 2013

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   13/11/2018 ISMB - Copyright 2013

Output (January) 13/11/2018 ISMB - Copyright 2013

PV block: detailed description Introduced integration for calculating self-production 13/11/2018 ISMB - Copyright 2013

Self-production (January vs July) 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

Introducing battery (2nd approach) Fine modeling of the energy storage Based on approaches described in literature 13/11/2018 ISMB - Copyright 2013

Introducing battery (2nd approach) 13/11/2018 ISMB - Copyright 2013

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 2032-6653 - © 2009 AVERE, EVS24 Stavanger, Norway, May 13 - 16, 2009 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

1 Battery with RLC load 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

2 Batteries with resistive load 13/11/2018 ISMB - Copyright 2013

Refining the control logic Actions driven by both SOC1 and SOC2 Swap Buy Sell do nothing 13/11/2018 ISMB - Copyright 2013

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 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

Table of truth - battery states B1 SOC B2 SOC B1 state Action + - DO NOTHING +/- BUY SWAP SELL State: charging discharging 13/11/2018 ISMB - Copyright 2013

Table of truth – Simulink implementation 13/11/2018 ISMB - Copyright 2013

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 13/11/2018 ISMB - Copyright 2013

ISMB - Copyright 2013