Energy Flexibility Data Model (EFDM) - Industrial Energy Management Dataset (Zenodo 2025)

Overview

The Energy Flexibility Data Model (EFDM) published on Zenodo in June 2025 provides a comprehensive framework and dataset for representing and exchanging energy flexibility information in industrial and commercial energy systems.

Concept and Motivation

• Energy flexibility refers to the ability to adjust energy consumption or production in response to external signals (price, grid constraints, renewable availability) without compromising core business operations.
• Industrial facilities possess significant flexibility potential through adjustable loads (e.g., electric arc furnaces, cooling systems, compressors), on-site generation (CHP, solar), and energy storage.
• Lack of standardized data models hinders the activation and monetization of flexibility in energy markets and grid services.

EFDM Components

• Flexibility assets: Identification and characterization of equipment with adjustable power consumption or generation (asset type, rated power, operating constraints).
• Flexibility profiles: Time-series representations of available upward/downward power adjustments, ramp rates, duration limits, and recovery times.
• Activation signals: External triggers (grid frequency, market prices, demand response events) and corresponding flexibility responses.
• State information: Current operational state, committed flexibility offers, and historical activation performance.
• Economic parameters: Cost functions, revenue models, and constraints for flexibility provision.

Data Model Structure

• JSON or XML schema defining standard fields, data types, units, and relationships for interoperability.
• Hierarchical organization: company → site → production line → individual asset, enabling aggregation at different levels.
• Time-series format with configurable resolution (seconds to hours) depending on application (real-time control vs. day-ahead planning).
• Metadata tags for asset categories, industry sectors, and geographic locations to facilitate matching with grid services.

Use Cases

• Demand response programs: Industrial companies communicating available flexibility to utilities and aggregators for grid balancing and peak reduction.
• Energy trading: Participating in wholesale energy markets by offering flexible load or generation as ancillary services.
• Sector coupling: Coordinating electricity, heat, and gas systems to optimize multi-energy industrial sites (e.g., power-to-heat, electrolysis).
• Internal energy management: Optimizing production schedules and equipment dispatch within a company based on dynamic energy prices and renewable availability.
• Grid integration: Enabling distribution system operators to aggregate and activate industrial flexibility for local congestion management and voltage control.