An Adaptive Load Management System using Predictive Control Optimization

TL;DR

This paper introduces an adaptive, predictive control-based load management system for air-conditioning that optimizes power consumption, responds to market prices, and demonstrates promising savings through numerical testing on real data.

Contribution

The paper presents a novel autonomous load management system using predictive control optimization tailored for air-conditioning, with real data validation and potential for energy resource efficiency.

Findings

Effective power consumption optimization demonstrated on real ISO data

Significant energy savings observed in numerical simulations

System responsiveness to market price fluctuations confirmed

Abstract

In this paper an adaptive load management system that uses predictive control optimization is introduced. This price elastic system is able to optimize the consumption of power and is fully autonomous and responsive to market clearing prices. The area of application chosen was an air-conditioning system that allows the end user to select a comfort zone that serves as the boundary conditions for the optimization algorithm. The temperature function that governs our algorithm is also derived and tested. Numerical examples are then presented to show the effectiveness of this system on day-ahead and real-time data from ISOs. The developed system showed promising results and savings that will improve the utilization of present energy resources. Finally, the implementation of this system was discussed and some preliminary modeling was performed to show the potential realization of such a system