Dissecting demand response mechanisms: the role of consumption forecasts and personalized offers

TL;DR

This paper critically analyzes demand response mechanisms in smart grids, highlighting the limitations of macroscopic models, introducing detailed microscopic models, and evaluating the impact of forecast errors on mechanism performance.

Contribution

It provides a detailed microscopic modeling framework for DR mechanisms and assesses their complexity and sensitivity to demand forecast errors, contrasting with prior macroscopic approaches.

Findings

Macroscopic models hide important assumptions affecting implementation.

Optimizing DR mechanisms is complex and requires heuristics.

Forecast errors significantly impact the effectiveness of DR strategies.

Abstract

Demand-Response (DR) programs, whereby users of an electricity network are encouraged by economic incentives to rearrange their consumption in order to reduce production costs, are envisioned to be a key feature of the smart grid paradigm. Several recent works proposed DR mechanisms and used analytical models to derive optimal incentives. Most of these works, however, rely on a macroscopic description of the population that does not model individual choices of users. In this paper, we conduct a detailed analysis of those models and we argue that the macroscopic descriptions hide important assumptions that can jeopardize the mechanisms' implementation (such as the ability to make personalized offers and to perfectly estimate the demand that is moved from a timeslot to another). Then, we start from a microscopic description that explicitly models each user's decision. We introduce four DR mechanisms with various assumptions on the provider's capabilities. Contrarily to previous studies, we find that the optimization problems that result from our mechanisms are complex and can be solved numerically only through a heuristic. We present numerical simulations that compare the different mechanisms and their sensitivity to forecast errors. At a high level, our results show that the performance of DR mechanisms under reasonable assumptions on the provider's capabilities are significantly lower than