When Renewable Energy Meets Building Thermal Mass: A Real-time Load Management Scheme

TL;DR

This paper presents a stochastic optimization approach for real-time power management in renewable-powered smart buildings, balancing user satisfaction and energy consumption under noisy conditions.

Contribution

It introduces a novel profit model and a Bregmen projection-based mirror descent algorithm with proven convergence for optimal power management in noisy environments.

Findings

The proposed algorithm effectively manages power under noise.

The model balances user satisfaction with energy efficiency.

Convergence proof guarantees algorithm reliability.

Abstract

We consider the optimal power management in renewable driven smart building MicroGrid under noise corrupted conditions as a stochastic optimization problem. We first propose our user satisfaction and electricity consumption balanced (USECB) profit model as the objective for optimal power management. We then cast the problem in noise corrupted conditions into the class of expectation maximizing in stochastic optimization problem with convex constraints. For this task, we design a Bregemen projection based mirror decent algorithm as an approximation solution to our stochastic optimization problem. Convergence and upper-bound of our algorithm with proof are also provided in our paper. We then conduct a broad type of experiment in our simulation to test the justification of our model as well as the effectiveness of our algorithm.