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Grid-interactive efficient buildings can provide many benefits for the power grid. However, it is very challenging to coordinate their operations intelligently due to many considerations, e.g., privacy protection and scalability. To this end, this paper investigates an optimal coordination operation problem of multiple grid-interactive residential buildings under uncertainties to minimize the total peak demand violation of all residential buildings during the grid service periods while minimizing their respective operation cost. Taking into account multi-source uncertainties, temporal and spatial couplings, inexplicit thermal dynamics models, and the lack of information sharing among residential buildings incurred by privacy protection, it is difficult to design a centralized model-based optimization operation method. To overcome the challenge, we design an online distributed coordination operation algorithm based on the alternating direction method of multipliers (ADMM) and relative entropy regularized policy iteration (RERPI). The designed algorithm consists of two stages, i.e., independent offline training of each RERPI-based agent related to the building energy management system controller (BEMSC) in each residential building (stage-1) and online distributed execution for coordination between the aggregator controller and each BEMSC under the ADMM framework (stage-2). Simulation results verify the effectiveness and scalability of the designed algorithm.

Exploiting the potential for aggregation regulation of virtual power plants (VPPs) on the distribution network side to support the frequency regulation (FR) of the main grid is an essential research topic. However, in the process of implementing FR through communication networks, the imperfect communication may affect the rapid power support performance of the VPP. As such, we propose a cyber-physical collaboration based control method. In the physical layer, in order to effectively respond to power regulation instructions, a dispatch-based control strategy is presented according to the adjustment capacity of the VPP. In the cyber layer, a joint spectrum scheduling and relay power allocation method is developed to mitigate the impact of network uncertainty and meet communication demands. Finally, extensive simulation results verify the effectiveness of the proposed method.

When there is a sudden load disturbance in an islanded microgrid, the peer-to-peer control model requires the energy resource to maintain a margin of generation, resulting in a relatively limited regulation range, that is, voltage/frequency sometimes requires additional control to maintain stability. A “source–storage–load” coordinated master–slave control strategy is proposed in this study to address the aforementioned issues. The system voltage and frequency will be stable as long as the output frequency and voltage of the master resource are stable. Furthermore, it can fully utilize the power supply capacity of resources to support the supply–demand balance. The following tasks are included in the proposed strategy: 1) to improve the operational security in the face of load disruption, a source–storage–load coordinated control method based on the “ramping speed” ratio is proposed, which can quickly restore the balance of supply and demand; 2) to improve the communication reliability in the face of interruption, a channel planning method is proposed, which can address the communication interruption problem by constructing an internal network among source–storage–load; and 3) to improve the mode switching stability of resources subjected to external disturbance, the external disturbance suppression and stability analysis involved in the regulation process are completed using sliding-mode control and small signal model methods. Related case studies are carried out to verify the effectiveness of the proposed strategies.

Formation control is a hot topic in multiagent systems (MASs). In this article, a bipartite time-varying prescribed range formation is developed for a class of nonlinear MASs with unknown disturbances. The time-varying prescribed range formation means that agents achieve a formation shape within a predefined region relative to their neighbors, and the region changes along with the time-varying formation distance. An error transformation is introduced to transform this bipartite prescribed range formation problem into a stabilization control one. A surface-error-based predictor is developed to generate prediction errors for update of neural networks (NNs), and this form decouples approximation of system dynamics and control signal. With prescribed range information and signed graph, a disturbance observer with predictor-based NNs is constructed to compensate external disturbances and NNs' approximation errors. Also, a normalization learning method is employed to reduce the number of NN's learning parameters. With Lyapunov-based stability analysis, it is proven that a bipartite time-varying formation is achieved within a prescribed range. Simulation results with a group of quadrotors verify the effectiveness of the proposed bipartite control scheme.