An Optimal Treatment Assignment Strategy to Evaluate Demand Response Effect

TL;DR

This paper develops an optimal strategy for assigning demand response signals to accurately estimate their impact on electricity consumption, overcoming high-dimensional challenges with limited data.

Contribution

It introduces a tractable algorithm for strategic signal assignment that minimizes estimation variance, outperforming traditional randomized methods in high-dimensional settings.

Findings

The proposed algorithm achieves optimal variance reduction.

Randomized assignment is less efficient with many covariates.

Simulation results validate the effectiveness of the strategy.

Abstract

Demand response is designed to motivate electricity customers to modify their loads at critical time periods. The accurate estimation of impact of demand response signals to customers' consumption is central to any successful program. In practice, learning these response is nontrivial because operators can only send a limited number of signals. In addition, customer behavior also depends on a large number of exogenous covariates. These two features lead to a high dimensional inference problem with limited number of observations. In this paper, we formulate this problem by using a multivariate linear model and adopt an experimental design approach to estimate the impact of demand response signals. We show that randomized assignment, which is widely used to estimate the average treatment effect, is not efficient in reducing the variance of the estimator when a large number of covariates is present. In contrast, we present a tractable algorithm that strategically assigns demand response signals to customers. This algorithm achieves the optimal reduction in estimation variance, independent of the number of covariates. The results are validated from simulations on synthetic data.