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

• Published in: Journal of modern power systems and clean energy Vol. 7; no. 3; pp. 568 - 578
• Main Authors: Chen, Yingying, Luo, Fengji, Dong, Zhaoyang, Meng, Ke, Ranzi, Gianluca, Wong, Kit Po
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); IEEE
• Subjects: Computer simulation; Demand response; Demand side management; Direct load control; Electrical Machines and Networks; Energy; Energy Systems; Evolutionary algorithms; Heuristic methods; Inertia; Load shedding; Optimization; Peak load; Power Electronics; Renewable and Green Energy; Schedules; Scheduling; Specific heat; Stability; Thermal comfort; Thermal comfort model; Thermostatically controlled load; Direct load control; Demand side management; Demand response; Thermal comfort model; Thermostatically controlled load
• ISSN: 2196-5625; 2196-5420
• DOI: 10.1007/s40565-018-0431-3

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.