A Data-driven Bidding Model for a Cluster of Price-responsive Consumers of Electricity

TL;DR

This paper introduces a data-driven inverse optimization method to accurately model the market bid of a cluster of price-responsive electricity consumers, incorporating exogenous factors for improved forecasting and market participation.

Contribution

It presents a novel bilevel programming approach that estimates complex market bids from price-consumption data, including external influences like weather and calendar effects.

Findings

Successfully captures the price-sensitive behavior of consumer clusters

Enables accurate forecasting of power consumption for market participation

Demonstrates effectiveness with real-world household data

Abstract

This paper deals with the market-bidding problem of a cluster of price-responsive consumers of electricity. We develop an inverse optimization scheme that, recast as a bilevel programming problem, uses price-consumption data to estimate the complex market bid that best captures the price-response of the cluster. The complex market bid is defined as a series of marginal utility functions plus some constraints on demand, such as maximum pick-up and drop-off rates. The proposed modeling approach also leverages information on exogenous factors that may influence the consumption behavior of the cluster, e.g., weather conditions and calendar effects. We test the proposed methodology for a particular application: forecasting the power consumption of a small aggregation of households that took part in the Olympic Peninsula project. Results show that the price-sensitive consumption of the cluster of flexible loads can be largely captured in the form of a complex market bid, so that this could be ultimately used for the cluster to participate in the wholesale electricity market.