Individual microgrids have proven their ability to provide uninterrupted power to critical end-use load during severe events. Building on the performance of individual microgrids during extreme events, there has been an increasing interest in the operations of network microgrids. By networking microgrids during extreme events, it is possible to share resources, increase the duration for which they can operate islanded, and increase the resiliency of critical end-use loads. While there are benefits to networking the operations of resiliency-based microgrids, the switching operations that they require introduce transients which can result in a loss of dynamic stability. The issue of dynamic stability is especially acute in microgrids with high penetrations of inverter-connected generation, and a correspondingly low system inertia. While the low-inertia of these microgrids can be increased by over-sizing the rotating generators, the increased capital and operating costs can become a barrier to deployment. This paper will present a method of augmenting primary frequency controls to support the switching transients necessary for the operation of networked microgrids, without the need to over-size rotating machines.

This investigation proposes an energy management system for large multizone commercial buildings that combines distributed optimization with the adaptive learning. While the distributed optimization provides scalability and models the fresh-air infusion as ventilation constraints, the learning algorithm simultaneously captures the influences of occupancy and user interactions. The approach employs a hierarchical architecture and uses a service-oriented framework to propose a distributed optimization method for commercial buildings. In addition, it also includes operational constraints required for optimizing the building energy consumption not studied in the literature. We show that our hierarchical architecture provides much better scalability and minimal performance loss comparable to the centralized approach. We illustrate that the influences of operational constraints on chiller, duct, damper, and ventilation are important for studying the energy savings. The energy saving potential of the proposed approach is illustrated on a 10-zone building, while its scalability is shown via simulations on a 500-zone building. To study the robustness of the approach meeting cancellations or other events that influence zone thermal dynamics, the resulting energy savings are studied. The results demonstrate the advantages of the proposed algorithm in terms of scalability, energy consumption, and robustness.

Restoration of end-use loads during bulk transmission system outages is a key challenge for electric utilities. During those outages, utilities often do not have enough resources to fully restore the end-use loads. System reconfiguration can provide access to non-utility assets that can enable restoration; however, not all available switching options are feasible due to voltage and load limits. Engaging non-utility assets for system restoration during system events requires commensurate compensation and incentive calculation. This paper presents a new transactive energy system framework (TESF) to incentivize distributed energy resources (DERs) to provide reactive power for supporting the self-healing functions of distribution management system (DMS). TESF engages non-utility DERs during resiliency events by incentivizing them through transactive signals computed using marginal cost of producing reactive power and marginal benefit of additional switching operations. TESF calculates additional switching options, which DMS then leverages to identify new optimal switching plans to restore additional loads. A test system consisting of multiple IEEE-13 node distribution feeders is used to demonstrate the performance of the proposed method. For the given system configuration and use-case, TESF results in 24.82% additional load restoration and one additional switching operation.

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.