ArXiv TLDR

On the Design of Stochastic Electricity Auctions

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2604.13603

Thomas Hübner

econ.GNecon.THeess.SY

TLDR

This paper proposes a new design for day-ahead electricity auctions that accounts for renewable energy uncertainty by conditioning contracts on "states of the world."

Key contributions

  • Addresses inefficiency in day-ahead electricity auctions caused by renewable energy uncertainty.
  • Introduces contracts conditioned on "states of the world" using equilibrium under uncertainty theory.
  • Develops criteria for defining optimal "states of the world" via an optimal partitioning problem.
  • Demonstrates the method using a case study of offshore wind farms in the European North Sea.

Why it matters

Current day-ahead electricity auctions fail to account for renewable energy uncertainty, leading to inefficient use and suboptimal system decisions. This paper offers a novel auction design that conditions contracts on future "states of the world," significantly improving renewable energy integration and market efficiency.

Original Abstract

Electricity is typically traded in day-ahead auctions because many power system decisions, such as unit commitment, must be made in advance. However, when wind and solar generators sell power one day ahead, they face uncertainty about their actual production. In current day-ahead auctions, this uncertainty cannot be directly communicated, leading to inefficient use of renewable energy and suboptimal system decisions. We show how this problem can be addressed using the concept of equilibrium under uncertainty from microeconomic theory. In particular, we demonstrate that electricity contracts should be conditioned not only on the time and location of delivery, but also on the state of the world (e.g., whether it will be windy or calm). This requires a precise definition of the state of the world. Since there are infinitely many possible definitions, criteria are needed to select among them. We develop such criteria and show that the resulting states correspond to solutions of an optimal partitioning problem. Finally, we illustrate how these states can be computed and interpreted using a case study of offshore wind farms in the European North Sea.

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