Day-ahead TCLs dispatch optimization: An integer genetic algorithm approach based on microgrids composition

• TCL optimization to improve microgrids performance. • Tailored optimization functions adapted to diverse microgrid compositions. • Addressing demand response and real-time mismatches for optimized TCLs via ON/OFF sequences. • Innovative use of stratified randomness sampling in GA’s initial population. Day-ahead microgrid optimization has been extensively studied in recent technical literature, which predominantly focuses on microgrids comprised of loads, renewable energy systems (RES), and energy storage systems (ESS). However, many microgrids are only composed of loads (such as homes in buildings). This work studies microgrid optimization through a specific focus on thermostatically controllable loads (TCLs), prevalent components in such microgrids. The optimization objectives are tailored to account for the unique characteristics of each microgrid’s composition. Additionally, the study considers the TCLs’ ability to participate in demand response programs within the power system and addresses challenges stemming from discrepancies between day-ahead dispatch and real-time operation. Importantly, the optimization process employs a genetic algorithm (GA) to derive optimal on/off sequences and corresponding temperature profiles for each TCL, instead of adjusting variable temperature setpoints. Furthermore, the GA initial population is generated using a novel method called stratified random sampling, proposed in this work. The study presents a procedure for TCL optimization aimed at maximizing each microgrid’s performance relative to its composition. Results demonstrate a reduction in the targeted metric ranging from 2.4% to 18%.