Optimizing Individualized Incentives from Grid Measurements and Limited Knowledge of Agent Behavior

TL;DR

This paper develops a flexible incentive optimization framework for grid resources that accounts for unknown human responses, using feedback algorithms that converge to near-optimal incentives even with limited data and complex behaviors.

Contribution

It introduces a novel constrained optimization model for incentives that treats human behavior as an unknown function and proposes feedback algorithms with convergence guarantees.

Findings

Algorithms achieve near-optimal incentives in simulations.

Effective even when assumptions like convexity are violated.

Demonstrates practical applicability in voltage regulation scenarios.

Abstract

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.