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

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 392 000 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.

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

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

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

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)

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

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

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

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

Contact Mathieu Bordigoni T : +33 (0)1 81 97 53 93 mathieu.bordigoni@enedis.fr

Annexes

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

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

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

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