IA Symposium 2018

Titre

Smart Cities + Energy + AI :

EDF Labs perspective

Stéphane Tanguy, EDF Labs CIO

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IA Symposium 2018

Summary

1. EDF Labs introduction

2. EDF Smart City platform

3. Singapore HDB project results

4. Electric mobility

5. Smart charging and AI

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IA Symposium 2018 3

ELECTRIC Transition

CLIMATE Change

DIGITAL & SOCIAL Transition

Major Forces disrupting the utility industry

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ELECTRIC Transition

CLIMATE Change

DIGITAL & SOCIAL Transition

EDF Labs 4 strategic priorities

Source: Newman&Kenworthy

Urban planning as a foundation of Smart CitiesWhy energy providers should get involved from planning stage

Smart cities : energy & comfort

Noise

Results from CEREMA, Study for EIFER &EDF,

2015

Air pollution

Results from Centrale Lyon, Study for EIFER

&EDF, 2015

Urban heat Island

Image LANDSAT des températures à Strasbourg, le

14 juillet 2013. - Capture d’écran ADEUS

Smart cities : supporting urban project introducing energy planning

Prospective for urban

developement

Exogenousscenarios

User scenarios

Simulation+ + =

Initiatives: parameters,

time, location, describing

investments, incentives, or urban policies

Evolution of oil price,

population, interest rates, temperatures

Integratedmodel with

multiple interactions at

each time step

Resultscorresponding

to eachscnenario: Decisionsupport

Time T1

Building refurbishment applied on neighborhood A

Time T2

PV investments applied on buildings X1+X2+…+X22

Time T3

District heating applied on neighborhood B+C+D

Prospective

I EDF I 2013

© 2

012

TH

OM

AS

SINGAPOUR SINGAPORE HDB PROJECT

I EDF I 201310

› Singapour, Housing & Development Board› Refurbishment› New neighborhoods

Strategic recommendationsKPI definitionTechnology choicesScenariosLong term impacts

Decision support platform Initiative parameters Precise scenarios Impact simulation Results analysis 3D visualisation

Yuhua (refurbish.)– 38 buildings

……(new) – 800 buildings

SINGAPORE HDB PROJECT

Implement Greenery to reduce cooling needs

Green Home Package to improve energy efficiency of appliances

Improve technology of Light in common areas with LED

Install PV panels to produce local energy Energy

EfficiencyUp to -12% Energy

Cons.

Green Energy~ 2,700 MWh/yr

prod.

CO2 -30 %

Costs SGD xxxK investment

PROJECT RESULTS

Electric Mobility : opportunity and challenges for cities and

utilities and where IA can help

Why Electric Vehicle charging should better be smart !

France 2035 scenario : 9 Million Electric Vehicles

• 30% of national vehicles fleet• 5% of national electricity consumption

• Without charging optimization : 20% of peak consumption

• without charging optimization : 5% of peak consumption !!

Smart management of EV charging offers multiple services opportunity for the electric system :

• Supply and demand equilibrium• Frequency and voltage regulation

• Absorption of renewable production surplus

Plug & chargeBasic control

with price signals

Advanced control at the building

level

Advanced control at the electricsystem level

Advanced control with bidirectional

power flow

Different smart charging strategies to accompany each step of EV roll-out:

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Problem statement : How to optimize district EV charging in order to take into

account electric network constraints ?

Proposal : leverage reinforcement learning to train an EV charging algorythm

(home controller) taking into account home electricity consumption, mobility

habits, home presence, price signal ..

Results after 300 iterations : vehicle always charged, cost optimized, 1 charge

period

Perspectives :

- Adapt algorythm to specific EV owner requirement (tipical schedule, charging

rish aversion, cost aversion ..)

- Fleet management with Pmax constraint (charging operator, 1 central

controller)

- Complete district optimization with PowerFlow constraint (DSO use case)

- Multi agent modelisation at district scale

Leveraging AI to turn charging smart !