Carbon-Aware Scheduling of Thermostatically Controlled Loads: A Bilevel DRCC Approach
Yijie Yang, Jian Shi, Dan Wang, Chenye Wu, Zhu Han
Abstract
Thermostatically controlled loads (TCLs), including air conditioners, heat pumps, water heaters, and refrigerators, play a pivotal role in demand response due to their thermal inertia and inherent flexibility. TCLs also substantially impact energy consumption and emissions within commercial and residential buildings, which makes them critical for the low-/zero-carbon transition that the building sector is undergoing to meet global climate objectives. To aid in this process, this paper proposes a carbon-aware robust scheduling approach for TCLs. The proposed approach precisely models carbon emissions attributed to TCLs, and formulates TCL scheduling as a distributionally robust chance-constrained (DRCC) optimization problem to ensure robust decision-making. We then develop a novel bilevel optimization reformulation strategy to address challenges such as over-conservatism and computational intractability that often arise from solving DRCC problems using conventional approaches. Real-world data evaluation demonstrates significant reductions in costs and carbon emissions compared to state-of-the-art methods, showcasing the effectiveness of our approach in potentially decarbonizing the building sector.