January 23, 2024

Quantifying Demand Flexibility: Towards a Standardised Approach


Centre for Net Zero, Enedis, Enel X, National Grid Electricity Distribution, Octopus Energy


As we transition to an energy system powered by an increasing number of renewables, we’ll rely on demand flexibility more – when consumers shift their electricity consumption to support the system. As flexibility services expand, we need robust and common methods underpinning them to establish a fair playing field for market participants and consumers.

This paper outlines a potential set of common principles for quantifying demand flexibility, considering different methods and when these might be appropriate, and calls for further collaboration to coalesce around a more standardised approach to baselining in future. It draws on contributions from Centre for Net Zero, Enedis, Enel X and National Grid Electricity Distribution and Octopus Energy, as well as discussion with a range of parties across industry.

Key findings

① We identify four baselining principles, rather than one optimal methodology, allowing us to balance trade-offs between competing objectives for different use cases

  • Accuracy – ensuring that the flexibility provided is neither overestimated for the party buying it, nor underestimated for the party providing it.
  • Fairness – limiting opportunities to “game” baselines, while ensuring consistency for consumers and a level playing field for all market participants.
  • Simplicity – making it straightforward to implement, replicate, understand and verify from available data.
  • Interoperability – enabling easy access to different flexibility services, and a common framework for coordinating this.

② We map out five broad baselining archetypes, commonly used by market participants

  • Fixed baseline – assuming the “‘normal”’ consumption level of an asset (i.e. with a fixed profile), with any deviation from this regarded as delivery.
  • Meter-before, meter-after – using actual load data immediately preceding a flexibility instruction as a proxy for what the consumer would otherwise have used.
  • Consumption profile – using recent historical meter data to construct a counterfactual for what the consumer(s) would otherwise have used.
  • Control group – using statistical sampling to create a counterfactual for a portfolio of customers, based on data from similar customers who are not providing flexibility
  • Nomination – a flexibility service provider generating a subjective forecast for the dispatch window when flexibility is to be provided.

③ Our recommended next steps centre on building consensus around a common set of principles and guidelines

  • Set up a baselining working group, made up of both buyers and sellers of flexibility, with a core focus of agreeing on a set of common principles.
  • Establish good practice guidelines for market operators now and consider potential rules to regulate baselining in future aligned with the agreed principles.
  • Develop a “library of baselines” to guide those buying flexibility to identify suitable baselines for the mix of customer archetypes in the future energy system.