Conference Year



Transactive control, stochastic optimization, thermal energy storage, uncertainty, model predictive control


With the increasing adoption of renewable energy and electric vehicles in the power grid, dealing with uncertainty in both supply and demand is critical to ensuring reliable and efficient operations. In this study, we discuss the value of a twostage stochastic control framework for an aggregator to address such problems by promoting improved decisionmaking and performance despite inherent uncertainty. An uncertaintyaware transactive control framework was developed to account for uncertainties in future conditions due to occupancy patterns, weather conditions, onsite power generation, and realtime pricing schemes. In the dayahead period, the aggregator decides the electricity procurement plan considering the possible realtime control strategies for operation of the commercial building thermal energy storage (TES) assets and residential building electric water heaters. During realtime operations, the aggregator modulates controllable loads based on transactive market mechanisms with model predictive control (MPC). In order to evaluate the performance, this study quantified the expected value of perfect information (EVPI) and the value of the stochastic solution (VSS) to analyze the cost of uncertain information and potential benefits of solving the stochastic optimal control problem. This paper demonstrates how the stochastic solution of the developed framework can provide useful information for customers and grid operators in the management of uncertain situations to support grid reliability and sustainability.