Slide 1: Load, EV, and HP
Load, EV, and HP Task Force Local Redistribution of Injections and Offtakes (LRIO) September 4th, 2025
Slide 2: Load Agenda
Load Agenda A Generation & Storage Minutes Validation B Setting up the Scene C Building the Offtakes Reference Context D Known Load Evolution E Electric Vehicles F Heat Pumps G Next Steps
Slide 3: A. Kick-off Minutes Validation
A. Kick-off Minutes Validation
Slide 4: Generation & Storage Workshop Minutes Validation
Generation & Storage Workshop Minutes Validation
Slide 5: B. Setting Up the Context
-€ elia B. Setting Up the Context 96 + BR —__
Slide 6: Key Messages from the Previous Workshops
Key Messages from the Previous Workshops Kick-Off – May 15th Generation & Storage – June 25th Local Redistribution is a series of technology-specific methodologies that are used to assign a node to a current or Generation & Storage forecast mostly consists of units that are future usage, as well as
Slide 7: Local redistribution geographically splits aggregated grid injection & offtake values
Local redistribution is a process that geographically splits aggregated grid injection & offtake values, with the aim to assign a potential transmission grid connection point. Factors influencing additional uses or repartition keys for aggregates references - non exhaustive • Evolution of existing U
Slide 8: B. Building the Offtakes Reference Context
B. Building the Offtakes Reference Context Presentation 8 title
Slide 9: The Reference Context consists of the Reference Grid, Static Data and expected injections
The Reference Context consists of the Reference Grid, Static Data and the expected injections or offtakes for each hour of the forecasted horizon, for each substation Macro-level Offtakes scenario Based on metered data, specific planned evolution Grid Model static Quantification of Offtakes and macr
Slide 10: The Offtake Reference Context is built from existing offtakes, planned evolutions & redistributed targets
The Offtake Reference Context is built from existing offtakes, specific planned evolutions & redistributed aggregated macro targets. Example of a national consumption scenario Unknown location, remaining potential of a category is redistributed after removal of the known offtakes for a given technol
Slide 11: The Offtake Reference Context is built from existing offtakes, planned evolutions & redistributed targets
The Offtake Reference Context is built from existing offtakes, specific planned evolutions & redistributed aggregated macro targets. Metered existing offtakes of the previous year Planned Evolution Based on client scenarios Electric Vehicles - Variation of current consumption Redistributed aggregate
Slide 12: Reference Year Preparation
Reference Year Preparation Presentation 12 title
Slide 13: The Reference Year preparation starts with a complete year of metered data
Reference Year Construction The Reference Year preparation starts with a complete year of metered data – The Reference Year starts on March 1st and last till Feb 28th or Feb 29th – Collection of hourly data at LD level. 2022-03-01 2022-03-01 ... Sector – Corrected for n-1 00:00:00 01:00:00 ... ABCNO
Slide 14: Thermosensitivity normalizes consumption from residential nodes (1/2)
Reference Year Construction Thermosensitivity normalizes the consumption attributable to heating and cooling from residential nodes (1/2) A. Definition of an average addition consumption per heating °C or cooling °C, by node, by season and by day type, based on historical data Data are aggregated by
Slide 15: Thermosensitivity normalizes consumption from residential nodes (2/2)
Reference Year Construction Thermosensitivity normalizes the consumption attributable to heating and cooling from residential nodes (2/2) B. Use of the coefficients for the Reference Year Temporal categorization information The difference between HD for the 1 For each node, heating-degrees (HD) are
Slide 16: Individual Growth Rates can be applied to each node, on an hourly basis
Reference Year Construction Individual Growth Rates can be applied to each node, on an hourly basis, when there are assumptions that exceptional variations in consumption will reasonably recover partially or totally. – The origin of the variation must be temporary and exceptional, it could be attrib
Slide 17: D. Planned Evolution
-€ elia D. Planned Evolution
Slide 18: Planned Evolutions gathers bottom-up information from customers' offtakes
Forecasts Planned Evolutions gathers bottom-up information from customers’ offtakes (direct clients and DSOs) for each node. 1 Collection of Bottom-Up Information Collection of consumption forecasts at nodal level: 2 Encoding & Consolidation of the data Type Year Power Cosφ Source Destination … Dire
Slide 19: Load Management tool collects bottom-up forecasts
Forecasts Load Management | Load management tool collects bottom-up forecasts, key for a well-designed system! Goal: better bottom-up forecast for win-win situation ! How can the grid users fill in their data? Via the Load Management tool ! • Accessible through your portal EPIC • KAMs challenge the
Slide 20: Grid users can access the load management tool via EPIC portal
Forecasts Load Management | Grid users can access the load management tool via EPIC portal, that is divided in 5 tabs and 3 angles of view Company X / Access point Y / Load management exercise 2024 CURRENT PROFILE FUTURE PROFILE FLEX Review last 12 months Assets Forecast Power Forecast Energy Flexib
Slide 21: Example of forecast table in the EPIC Portal (power)
Forecasts Example of forecast table in the EPIC Portal (power) Load | Task Force LRIO 21
Slide 22: Client best-estimate is defined based on realigned load management
Forecasts Client best-estimate is defined based on realigned load management – Client scenarios are used in the definition of the macro-scenarios, as bottom-up information source. Example of Client Scenarios (over time) – 15 customers lead to most TWh increase (~= 80% of the 70 increase in consumpti
Slide 23: DSO Planned Evolutions at coupling points are gathered in Blackbooks
Forecasts DSO : Planned Evolutions at the coupling points with distribution networks are gathered in Blackbooks. Preparation of the Reference & Meetings between DSOs and Elia Compilation of the final reports 1 Forecasts reports Elia prepares the Reference & Forecast Reports. They contain the measure
Slide 24: Sectoral Growth scaling of consumption
Forecasts For each sector, discrepancies between selected client scenarios and Sectorial Growth foreseen in Macro-Scenarios is solved thanks to homothetical scaling of the consumption of the sector. Application of Sectoral Growth Considered Sectors Rates (illustration) – Currently: Residential, Indu
Slide 25: D. Electric Vehicles
-€ elia D. Electric Vehicles
Slide 26: Running Values for Electric Vehicles and Heat Pumps are computed separately from known loads
The Running Values for Electric Vehicles and Heat Pumps are computed separately from the known loads, using market-based rules Metered existing offtakes of the previous year Planned Evolution Based on client scenarios Electric Vehicles - Variation of current consumption Redistributed aggregated targ
Slide 27: Load of additional EVs is computed per commune and per hour, then redistributed to substations
For each year, the load associated to additional EVs is computed per commune and per hour, which is then redistributed to substations The aggregated load curves The EV Repartition model from the macro-scenarios are For each target year, data is combines this data with other then redistributed to the
Slide 28: From aggregated EV targets to expected local load
From aggregated EV targets to expected local load Vehicles Distribution [#BEV/commune] Energy Needs per vehicle [kWh/location] Working days, vacations days, Home/work trip matrix, yearly distance Aggregated number of cars, cars per travelled, BEV consumption, % household, cars per commune, registrat
Slide 29: The EV Repartition Model combines vehicle data with trip information and traffic data
The EV Repartition Model combines the vehicle-related data with trip information, points of interest, and traffic data to generate time series for the different charging types # BEVs per home commune Daily differentiated home consumption Time series for charging at home Trip Database Additional load
Slide 30: Specific load profiles are defined per charging mode for smart charging
To take smart charging into account, specific load profiles are defined per charging mode, using market-based rules if applicable. Profile Rationale Modelling V0 Charges when plugging Pre-defined time series Charging to minimize costs, depending on regional tariffs V1H Pre-defined time series and/or
Slide 31: Smart charging and seasonality by redistributing the hourly aggregated load curve
Smart charging and seasonality is computing by redistributing the hourly aggregated load curve from the macro-scenario, using the EV Repartition Model as base for the redistribution key. The share of each commune The repartition key computed 1 is computed for each hour, starting from the time series
Slide 32: The Commune to Substation Mapping distributes communal values to substations
The Commune to Substation Mapping distributes a value known at communal level to the relevant substations This mapping is represented as a 2-dimensional matrix that indicates the share of each substation used in each commune. The choice of the substations depends on the category to be redistributed
Slide 33: E. Heat Pumps
-€ elia E. Heat Pumps
Slide 34: Heat pump consumption time series are distributed between communes using a repartition key
Heat pump consumption (residential and tertiary) time series are given per region as output of the economic dispatch model for each Target Year, and are distributed between communes using a repartition key. Hourly Aggregated Consumption Hourly Consumption Time Time Series Series per commune. Hourly
Slide 35: H. Next Steps
-€ elia H. Next Steps
Slide 36: Next Steps
Next Steps 2025 2026 May June July Aug Sep Oct Nov Dec Jan Feb W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 W1 W2 W3 W4 15/05 24/6 4/09 3/10 17/11 4/12 8/01 Public Consultation Workshop 3: Workshop 4: Workshop 5: Kick-Off & Workshop 1: W