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A microgrid is a promising approach to provide clean, renewable, and reliable electricity by integrating various distributed generations and energy storage systems into power systems. However, highly intermittent renewable generations and various load demands pose new challenges to the optimal resource management in a microgrid. This paper proposes a fully distributed control strategy based on the consensus algorithm for the optimal resource management in an islanded microgrid. The proposed strategy is implemented through a multiagent system framework, which only requires information exchange among neighboring agents through a local network. The objective is achieved through a two-level control strategy. The upper control level is a consensus-based optimization algorithm that discovers the reference of optimal power generation or demand while maintaining the supply–demand balance. The lower control level is responsible for reference tracking of the associated component. Simulation results in the IEEE 14- and 162-bus systems are presented to demonstrate the effectiveness of the proposed control strategy. [ABSTRACT FROM PUBLISHER]

As the demand for computing power is quickly increasing in the automotive domain, car manufacturers and tier-one suppliers are gradually introducing multicore electronic control units (ECUs) in their electronic architectures. In addition, these multicore ECUs offer new features such as higher levels of parallelism, which ease the compliance with safety requirements such as the International Organization for Standardization (ISO) 26262 and the implementation of other automotive use cases. These new features involve greater complexity in the design, development, and verification of the software applications. Hence, car manufacturers and suppliers will require new tools and methodologies for deployment and validation. In this paper, we address the problem of sequencing numerous elementary software modules, called runnables, on a limited set of identical cores. We show how this problem can be addressed as the following two subproblems, which cannot optimally be solved due to their algorithmic complexity: 1) partitioning the set of runnables and 2) building the sequencing of the runnables on each core. We then present low-complexity heuristics to partition and build sequencer tasks that execute the runnable set on each core. Finally, we globally address the scheduling problem, at the ECU level, by discussing how we can extend this approach in cases where other OS tasks are scheduled on the same cores as the sequencer tasks. [ABSTRACT FROM PUBLISHER]