A day-ahead scheduling framework for thermostatically controlled loads with thermal inertia and thermal comfort model

Yingying Chen, Fengji Luo, Zhaoyang Dong, Ke Meng, Gianluca Ranzi, Kit Po Wong

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper proposes a day-ahead dispatch framework of thermostatically controlled loads (TCLs) for system peak load reduction. The proposed day-ahead scheduling framework estimates the user’s indoor thermal comfort degree through the building thermal inertia modelling. Based on the thermal comfort estimation, a day-ahead TCL scheduling model is formulated, which consists of 3 stages: ① TCL aggregator estimate their maximal controllable TCL capacities at each scheduling time interval by solving a optimization model; ② the system operator performs the day-ahead system dispatch to determine the load shedding instruction for each aggregator; and ③ the TCL aggregators schedules the ON/OFF control actions of the TCL groups based on the instruction from the system operator. A heuristic based optimization method, history driven differential evolution (HDDE) algorithm, is employed to solve the day-ahead dispatch model of the TCL aggregator side. Simulations are conducted to validate the proposed model.

Original languageEnglish
Pages (from-to)568-578
Number of pages11
JournalJournal of Modern Power Systems and Clean Energy
Volume7
Issue number3
DOIs
Publication statusPublished - 1 May 2019

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