<div class="section abstract"><div class="htmlview paragraph">The emerging need of building an efficient Electric Vehicle (EV) charging infrastructure requires the investigation of all aspects of Vehicle-Grid Integration (VGI), including the impact of EV charging on the grid, optimal EV charging control at scale, and communication interoperability. This paper presents a cloud-based simulation and testing platform for the development and Hardware-in-the-Loop (HIL) testing of VGI technologies. Although the HIL testing of a single charging station has been widely performed, the HIL testing of spatially distributed EV charging stations and communication interoperability is limited. To fill this gap, the presented platform is developed that consists of multiple subsystems: a real-time power system simulator (OPAL-RT), ISO 15118 EV Charge Scheduler System (EVCSS), and a Smart Energy Plaza (SEP) with various types of charging stations, solar panels, and energy storage systems. The subsystems can communicate with each other via message queuing telemetry transport communication (MQTT) protocol. The OPAL-RT is used to perform grid simulation and optimal EV charging energy management at the distribution grid level. It communicates with node level EVCSS and the SEP to collect real-time charging data and send charging power commands. The OPAL-RT can also communicate with transmission level controllers to provide grid services, such as frequency regulation. The EVCSS manages regional EV charging to limit the effects of clustered EV charging on the distribution grid. It uses standardized communication protocols: Open Charge Point Protocol 2.0 for charging station networks and ISO 15118 between EVs and charging stations. The modular open systems design approach of the platform allows the integration of EV charging control algorithms and hardware charging systems for performance evaluation and interoperability testing. The experimental test results show that the communication links of the platform work properly, and the EV charging control algorithms can respond to transmission level grid service request with minimal impact on local operations.</div></div>
In scientific computing and data science disciplines, it is often necessary to share application workflows and repeat results. Current tools containerize application workflows, and share the resulting container for repeating results. These tools, due to containerization, do improve sharing of results. However, they do not improve the efficiency of replay. In this paper, we present the multiversion replay problem, which arises when multiple versions of an application are containerized, and each version must be replayed to repeat results. To avoid executing each version separately, we develop CHEX , which checkpoints program state and determines when it is permissible to reuse program state across versions. It does so using system call-based execution lineage. Our capability to identify common computations across versions enables us to consider optimizing replay using an in-memory cache, based on a checkpoint-restore-switch system. We show the multiversion replay problem is NP-hard, and propose efficient heuristics for it. CHEX reduces overall replay time by sharing common computations but avoids storing a large number of checkpoints. We demonstrate that CHEX maintains lightweight package sharing, and improves the total time of multiversion replay by 50% on average.
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