# A Novel Demand Response Model and Method for Peak Reduction in Smart Grids -- PowerTAC

## TL;DR

This paper introduces a new demand response model for peak reduction in smart grids, utilizing incentive-based algorithms and real-world simulation to optimize load reduction strategies.

## Contribution

It presents a novel probabilistic model for agent response to incentives and develops algorithms for optimal and online learning of discounts in smart grid demand response.

## Key findings

- The RP function accurately models agent load reduction probability.
- The MJS--ExpResponse algorithm maximizes expected reduction under budget constraints.
- The online MJSUCB--ExpResponse algorithm achieves sublinear regret in learning RRs.

## Abstract

One of the widely used peak reduction methods in smart grids is demand response, where one analyzes the shift in customers' (agents') usage patterns in response to the signal from the distribution company. Often, these signals are in the form of incentives offered to agents. This work studies the effect of incentives on the probabilities of accepting such offers in a real-world smart grid simulator, PowerTAC. We first show that there exists a function that depicts the probability of an agent reducing its load as a function of the discounts offered to them. We call it reduction probability (RP). RP function is further parametrized by the rate of reduction (RR), which can differ for each agent. We provide an optimal algorithm, MJS--ExpResponse, that outputs the discounts to each agent by maximizing the expected reduction under a budget constraint. When RRs are unknown, we propose a Multi-Armed Bandit (MAB) based online algorithm, namely MJSUCB--ExpResponse, to learn RRs. Experimentally we show that it exhibits sublinear regret. Finally, we showcase the efficacy of the proposed algorithm in mitigating demand peaks in a real-world smart grid system using the PowerTAC simulator as a test bed.