# Constrained Thompson Sampling for Real-Time Electricity Pricing with Grid Reliability Constraints

arXiv: https://arxiv.org/abs/1908.07964 · HTML: https://mlsanity.com/paper/1908.07964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Causative Cyberattacks on Online Learning-based Automated Demand Response Systems](https://mlsanity.com/paper/2307.15175/) (2023)
- [Vaccine allocation policy optimization and budget sharing mechanism using Thompson sampling](https://mlsanity.com/paper/2109.10004/) (2021)

## Abstract (arXiv)

We consider the problem of an aggregator attempting to learn customers' load flexibility models while implementing a load shaping program by means of broadcasting daily dispatch signals. We adopt a multi-armed bandit formulation to account for the stochastic and unknown nature of customers' responses to dispatch signals. We propose a constrained Thompson sampling heuristic, Con-TS-RTP, that accounts for various possible aggregator objectives (e.g., to reduce demand at peak hours, integrate more intermittent renewable generation, track a desired daily load profile, etc) and takes into account the operational constraints of a distribution system to avoid potential grid failures as a result of uncertainty in the customers' response. We provide a discussion on the regret bounds for our algorithm as well as a discussion on the operational reliability of the distribution system's constraints being upheld throughout the learning process.