Overview

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.