Adaptive Pricing in Unit Commitment Under Load and Capacity Uncertainty

TL;DR

This paper introduces an adaptive pricing method for electricity markets that accounts for load and capacity uncertainties, eliminating uplift payments and reducing self-scheduling, thus improving market efficiency.

Contribution

It proposes the first adaptive pricing mechanism that responds to uncertainties and removes the need for uplift payments in electricity markets.

Findings

The proposed method adapts to load and capacity uncertainties.

It eliminates the need for uplift payments.

It discourages self-scheduling among generators.

Abstract

The increase of renewables in the grid and the volatility of the load create uncertainties in the day-ahead prices of electricity markets. Adaptive robust optimization (ARO) and stochastic optimization have been used to make commitment and dispatch decisions that adapt to the load and capacity uncertainty. These approaches have been successfully applied in practice but current pricing approaches used by US Independent System Operators (marginal pricing) and proposed in the literature (convex hull pricing) have two major disadvantages: a) they are deterministic in nature, that is they do not adapt to the load and capacity uncertainty, and b) require uplift payments to the generators that are typically determined by ad hoc procedures and create inefficiencies that motivate self-scheduling. In this work, we extend pay-as-bid and uniform pricing mechanisms to propose the first adaptive pricing method in electricity markets that adapts to the load and capacity uncertainty, eliminates post-market uplifts and deters self-scheduling, addressing both disadvantages.