1. Mathieu Bordigoni, ENEDIS, France
Long-term forecast of industrial electricity demand A DSO vision: Scale Really Matters !
Mathieu Bordigoni, ENEDIS, France
Singapore - Wednesday, June 21

2. Enedis, the main French DSO, independent subsidiary of EDF
Generation
Transmission
Distribution
Supply
Clients
2016 Key figures
5,7 GW PV capacity connected
1,3 million km of power lines
35 million customers
388 TWh delivered
new connections
39,000 employees
€ 3,1 billion investments
11 million technical interventions
9,9 GW of wind capacity connected
61 minutes SAIDI
Enedis is Europe’s biggest distribution system operator in terms of power line length.
System Average Interruption Duration Index (SAIDI) is commonly used as a reliability indicator by electric power utilities. SAIDI 2015 : 61 min.
Investments : € 3 billion vs 2 billion in 2008.
95% of renewable capacity (wind, PV, biomass…) is connected to the distribution network.
Enedis turnover : € 13,5 billion. EBITDA : € 3,8 billion.

3. Distribution System Operator (DSO) : Inside the Energy Transition
The distribution network is experiencing an unprecedented set of changes
Increased penetration of Distributed Energy Resources (DERs)
Further electrification of transport (EV) and heat (heat pump)
Proliferation of information and communications technologies (ICT)
The DSO needs to anticipate long-term electricity demand changes
DSO stakes, investments and communications, are local
National electricity demand forecasting is not enough
Local scale forecasting for the manufacturing sector is challenging
One industrial plant may represent a significant part of electricity consumption in a local circuit network
Industrial activity relies strongly on local, national and international conditions
Long-term forecast of industrial electricity demand – 06/21/2017

4. Manufacturing industry forecasting challenge :
Enedis Local Forecasting Tool : Exhaustive coverage of local electricity issues
Demography
Buildings
Manufacturing industry forecasting challenge :
Strong local impacts on electricity networks but economic activity linked to national and/or international markets
Impacting local projects
Electricity
use
Energy prospective
Renewables (wind, PV)
Industry
Focus of the presentation
National consumption
International competitiveness
Energy efficiency
Process and System Electrification
Transport, services, agriculture
Long-term forecast of industrial electricity demand – 06/21/2017

5. Decomposition of the main underlying factors of electricity demand in the industry
To identify main drivers of electricity consumption for highly disaggregated industrial sectors
Electricity demand forecasting towards 2035 for 90 different manufacturing sub-sectors in France
Use historical data to forecast future trends of drivers for each industrial sub-sector (i)
Econometrics for demand per habitant and energy efficiency
Extrapolation with a time power function (y = α(t-t0)β) for others drivers
Econometrics :
GDP per capita + time trend
National population (hab.)
Average demand per habitant (€/hab.)
International competitiveness (>1 net exports ; <1 net imports)
Energy efficiency (MWh/€)
National electricity consumption by industrial sector i
Process and System Electrification
Extrapolation of historical trend
Econometrics :
Production + time trend
External forecast (INSEE)
Extrapolation of historical trend

6. Historical data for each industrial sector
Data sources
French population
1994 to 2015 – INSEE (French office of statistics)
International exports and imports
1994 to 2015 – UN COMTRADE
Industrial production volume index (nominal value)
1994 to 2015 – INSEE
Energy consumption by energy types
2007 to 2013 – Enedis
Extrapolation with a time power function (y = α(t)β)

7. Demand per habitant in relation with revenues
Demand growth per habitant = time trend + national GDP per capita growth + control of asymmetrical behavior + inventory adjustments
Energy efficiency = time trend + short-term production changes
A significant part of energy efficiency in industry may be related to capacity utilization of plants
In the short-term, processes are generally not adjusted for optimizing energy efficiency
e.g. electric motors without variable-frequency drive (VFD)
Control for short-term changes of production in order to identify effective energy efficiency
Coefficient of determination :
The coefficient of determination, denoted R2 or r2 and pronounced "R squared", is a number that indicates the proportion of the variance in the dependent variable that is predictable from the independent variable(s).
Lagged effects on one year
Cochrane-Orcutt AR(1) regression
Long-term forecast of industrial electricity demand – 06/21/2017

8. Combination with local electricity consumption data
Forecast at local level is performed by linking :
National forecast outcomes for 90 industrial sub-sectors
Includes different scenarios for economic growth
Electricity demand data at the plant scale
Industrial plants connected to the distribution network (industrial consumers connected to the transport grid are not included)
No forecast for local distribution networks (not Enedis)
Local forecast of growth in industrial electricity demand up to 2035
In green : Increase in electricity consumption
In red : Decrease of electricity consumption
Long-term forecast of industrial electricity demand – 06/21/2017

9. At the local level, detailed sectoral and geographical data are necessary
Using aggregated sector forecasts may lead to very divergent local results
Test : Local forecast results with the same data and methodology but with different level of sectoral disaggregation
1st case : 90 different sub-sectors
2nd case : 13 aggregated sectors
Study for one French department (Essonne)
Comparison of forecast outcomes town by town
With 90 manufacturing sub-sectors
With 13 aggregated sectors
Grey area represents municipality without manufacturing activity

10. Long-term forecast of industrial electricity demand – 06/21/2017
Conclusions
Electricity distribution operators will be key drivers of energy transition in many countries
Necessity to improve anticipation of electricity demand changes at the local level
Local forecast for the manufacturing industry is challenging :
Strong local impacts on electricity networks
But economic activity linked to national and/or international markets
This study proposes a methodology to address the issue of the manufacturing sector
Based on the decomposition of the main underlying drivers of electricity demand
With detailed data on both ;
Sectoral scale : 90 sub-sectors for the manufacturing industry
Geographical scale : Electricity consumption for every town by sector
Further improvements
How to forecast discrete local opening or closure of an industrial plant ?
Enedis is using those tools to adapt its infrastructures and to build a shared vision of the regional evolution related to electricity with public authorities and various stakeholders
The implementation of such methods and tools is applicable for other regions in the world
Long-term forecast of industrial electricity demand – 06/21/2017

11. Contact
Mathieu Bordigoni
T : +33 (0)

12. Annexes

13. Long-term forecast of industrial electricity demand – 06/21/2017
High sensitivity of forecasting results to the level of sectoral disaggregation
Sub-sectors of a common branch have very different prospective trends
e.g. forecasting of electricity demand for the aggregated French food and beverage sector up to 2035 (+1.3%)
Mask strongly contrasted estimations for sub-sectors (from -20% to 45%)
Each manufacturing sector has a specific relationship with GDP changes
Some sectors are very reliant on GDP fluctuations, e.g. metals
Others depend on population growth for the most part, e.g. the food industry
Electricity consumption forecasts for the French food and beverage sub-sectors up to 2035
Electricity consumption forecasts for the French industry according to GDP growth scenarios
Long-term forecast of industrial electricity demand – 06/21/2017

14. National demand and GDP growth
Each industrial sector national demand has a specific relationship with GDP growth
Some sector’s demand mainly driven by population
e.g. Food and beverages
National demand very dependent on GDP growth
e.g. Machine-tools, Metals
Mean R² = 0.71
Titre de la présentation - Date

15. Drivers of growth in electricity consumption by industrial sectors
For each industrial subsectors (90), a wide range of drivers for changes in electricity demand
For graphical convenience, graphs below aggregate sub-sectors results
Evolution of national production
Evolution of electric intensity
Titre de la présentation - Date

16. Econometric results for the whole manufacturing sector
Titre de la présentation - Date