Bidding Aggregated Flexibility in European Electricity Auctions

TL;DR

The paper tackles the challenge of aggregating decentralized demand-side flexibility for European day-ahead and intraday auctions. It introduces a market-applicable method that uses price forecasts to identify economically relevant profiles and conveys them to the market via exclusive groups of block bids, enabling scalable and accurate bidding. Case study results for heat pumps in Losone show high aggregation efficiency (up to 98%), linear computation time growth with resources, and substantial cost savings, validating both unbundled and integrated utility scenarios. The work highlights practical implications for market operators and suggests policy-level guidance to expand the use of exclusive groups to better harness distributed flexibility.

Abstract

Bidding flexibility in day-ahead and intraday auctions would enable decentralized flexible resources, such as electric vehicles and heat pumps, to efficiently align their consumption with the intermittent generation of renewable energy. However, because these resources are individually too small to participate in those auctions directly, an aggregator (e.g., a utility) must act on their behalf. This requires aggregating many decentralized resources, which is a computationally challenging task. In this paper, we propose a computationally efficient and highly accurate method that is readily applicable to European day-ahead and intraday auctions. Distinct from existing methods, we aggregate only economically relevant power profiles, identified through price forecasts. The resulting flexibility is then conveyed to the market operator via exclusive groups of block bids. We evaluate our method for a utility serving the Swiss town of Losone, where flexibility from multiple heat pumps distributed across the grid must be aggregated and bid in the Swiss day-ahead auction. Results show that our method aggregates accurately, achieving 98% of the theoretically possible cost savings. This aggregation accuracy remains stable even as the number of heat pumps increases, while computation time grows only linearly, demonstrating strong scalability.

Figures (11)

• Figure 1: Exclusive group of three block bids.
• Figure 2: Aggregation steps between flexible resources, aggregator, and auction.
• Figure 3: Approximation of the aggregate feasible set (black) using our method (red) and an inner (green) as well as outer approximation (blue).
• Figure 4: Single-node RC building model.
• Figure 5: Representation of the Losone MV and LV distribution network.