Uncertainty-aware optimal dispatch of building thermal storage portfolios via smoothed variance-reduced accelerated gradient methods

The rapid penetration of uncertain renewable energy resources into the power grid has made generation planning and real-time power balancing a challenge, prompting the need for advanced control on the demand-side. Although a grid-integrated building portfolio has been studied by coordinating building-level flexible energy resources, to reap further benefits, the need to develop a scalable supervisory control framework with computational efficiency is paramount. In this work, we introduce an uncertainty-aware transactive control framework for a large-scale building portfolio with thermal energy storage (TES) using a decomposition-based approach. We propose a day-ahead decision making framework for the power procurement problem, which is cast as a two-stage stochastic optimization problem. To solve this problem, we propose a smoothed variance-reduced accelerated gradient method. Notably this framework allows for parallelization in computing the sampled gradient. Preliminary numerics demonstrate the scalability and computational efficiency of the proposed algorithm to apply to the control framework with respect to the existing algorithm. Substantial energy cost savings were observed for the stochastic control framework over all the validation scenarios. This study provides further insights into the supervisory controller development for building portfolio-based grid services that can help advance electrification goals through coordination of energy storage assets.