Randomized Customer-Sensitivity-Aware Control of Thermostatic Loads for Better Integration of Intermittent Renewables

Increased electric grid penetration of intermittent renewable energy sources has reduced the controllability of the generation side and created a need for more coordination between generation and load to maintain grid stability. Thermostatically controlled loads (TCLs) have long been seen as capable of providing a source of load flexibility. However, controlling thousands of small loads to create a better match between generation and consumption is a challenging task. Direct load control methods tend to be imprecise and invasive, while pricing-based methods can result in social push-back and produce unreliable results. Following an established trend aimed at limiting loads synchronization effects, a probabilistic control scheme is proposed. It is based on a novel type of aggregator-customer contracts. The latter are tailored a priori so as to account for a customer’s particular tolerance to loss of comfort versus interest in cost reduction. While through these contracts, aggregators have to obey preagreed constraints on their controls, the upside for them is that they can reliably anticipate the aggregate behaviors that their pool of loads can achieve. The control is decentralized via a single so-called pressure signal which is broadcast and acts locally, in a probabilistic manner, on thermostat set points. We demonstrate how the probabilistic nature of the control allows achieving a continuum of smooth potentially desirable aggregate load behaviors.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Control Systems Technology
Published
2024-12-04
DOI
https://doi.org/10.1109/tcst.2024.3505033
Primary Topic
Advanced Control Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Randomized Customer-Sensitivity-Aware Control of Thermostatic Loads for Better Integration of Intermittent Renewables

Roland P. Malhamé, Tanguy Julien
IEEE Transactions on Control Systems Technology
Advanced Control Systems Optimization
article

Randomized Customer-Sensitivity-Aware Control of Thermostatic Loads for Better Integration of Intermittent Renewables

Roland P. Malhamé, Tanguy Julien
article en

Abstract

Increased electric grid penetration of intermittent renewable energy sources has reduced the controllability of the generation side and created a need for more coordination between generation and load to maintain grid stability. Thermostatically controlled loads (TCLs) have long been seen as capable of providing a source of load flexibility. However, controlling thousands of small loads to create a better match between generation and consumption is a challenging task. Direct load control methods tend to be imprecise and invasive, while pricing-based methods can result in social push-back and produce unreliable results. Following an established trend aimed at limiting loads synchronization effects, a probabilistic control scheme is proposed. It is based on a novel type of aggregator-customer contracts. The latter are tailored a priori so as to account for a customer’s particular tolerance to loss of comfort versus interest in cost reduction. While through these contracts, aggregators have to obey preagreed constraints on their controls, the upside for them is that they can reliably anticipate the aggregate behaviors that their pool of loads can achieve. The control is decentralized via a single so-called pressure signal which is broadcast and acts locally, in a probabilistic manner, on thermostat set points. We demonstrate how the probabilistic nature of the control allows achieving a continuum of smooth potentially desirable aggregate load behaviors.

IEEE Transactions on Control Systems TechnologyVol. 33(2)
Polytechnique Montréal (CA)
Affordable and clean energy
Openalex Percentile: Top 74%
Advanced Control Systems Optimization
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.