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Increasing deployment of advanced sensing, controls, and communication infrastructure enables buildings to provide services to the power grid, leading to the concept of Grid-interactive Efficient Buildings. Since occupant activities and preferences primarily drive the availability and operational flexibility of building devices, there is a critical need to develop occupant-centric approaches that prioritize devices for providing grid services, while maintaining the desired end-use quality of service. In this paper, we present a decision-making framework that facilitates a building owner/operator to effectively prioritize loads for curtailment service under uncertainties, while minimizing any adverse impact on the occupants. The proposed framework uses a stochastic (Markov) model to represent the probabilistic behavior of device usage from power consumption data, and a load prioritization algorithm that dynamically ranks building loads using a stochastic multi-criteria decision-making algorithm. The proposed load prioritization framework is illustrated via numerical simulations in a residential building use-case, including plug-loads, air-conditioners and plug-in electric vehicle chargers, in the context of load curtailment as a grid service. Suitable metrics are proposed to evaluate the closed-loop performance of the proposed prioritization algorithm under various scenarios and design choices. Scalability of the proposed algorithm is established via computational analysis, while time-series plots are used for intuitive explanation of the ranking choices.

This paper proposes a novel distributed architecture for controlling Heating, Ventilation and Air conditioning (HVAC) systems in commercial buildings. Zone Modules use local models and measurements to compute requests for HVAC service over various future time windows. These requests are expressed in terms of the heating/cooling service required which we can conceptually regard as tokens. A Central Scheduler balances token requests and allocates tokens to each zone for the next time slot. This allocation attempts to minimize total energy consumption while respecting operational constraints. Zone modules update their local models based on the measured thermal responses resulting from allocated tokens, and recompute forward token requests. This proposed token based architecture is inspired by medium access control protocols in communication networks. It offers several advantages in the context of HVAC systems. The architecture is scalable to realistic buildings with 200-500 thermal zones, it is robust relative to non-stationary environmental conditions and unanticipated changes in user needs, and it is modular enabling low-cost deployment without requiring expensive custom thermal modeling of buildings. We develop the zone module algorithms for computing token requests and central scheduler algorithms to allocate tokens. Using simulation studies, we demonstrate that the performance loss of our token based scheduling strategy is modest in comparison to a fully centralized nonlinear optimal control scheme.

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Individual microgrids can improve the reliability of power systems during extreme events, and networked microgrids can further improve efficiency through resource sharing and increase the resilience of critical end-use loads. However, networked microgrid operations can be subject to large transients due to switching and end-use loads, which can cause dynamic instability and lead to system collapse. These transients are especially prevalent in microgrids with high penetrations of grid-following inverter-connected renewable energy resources, which do not provide the system inertia or fast frequency response needed to mitigate the transients. One potential mitigation is to engage the existing generator controls to reduce system voltage in response to a frequency deviation, thereby reducing load and improving primary frequency response. This study investigates the use of a reinforcement-learning-based controller trained over several switching transient scenarios to modify generator controls during large frequency deviations. Compared to previously used proportional-integral controllers, the proposed controller can improve primary frequency response while adapting to changes in system topologies and conditions.