WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Conference Object Citation - WoS: 18Citation - Scopus: 19Optimal Energy Management and Scheduling of a Microgrid in Grid-Connected and Islanded Modes(IEEE, 2019-09) Zacharia, L.; Tziovani, L.; Savva, M.; Hadjidemetriou, L.; Kyriakides, E.; Bintoudi, A. D.; Al-Agtash, S.Microgrids are becoming one of the main components of future smart grids. Ensuring their optimal and stable operation is of crucial importance and can be a challenging task. In this paper, two optimization algorithms are implemented for scheduling the microgrid operation in grid-connected and islanded modes, according to the priorities and objectives in each mode. For achieving an optimal operation at each mode, the proposed scheme is able to shed loads, define the generation level of the photovoltaics and regulate the charging/ discharging level of the Energy Storage System (ESS). The effectiveness of the proposed scheduling is demonstrated through an analytical real-time simulation, where various transitions between the grid-connected and islanded modes are considered. The results indicate that the proposed scheme is able to regulate successfully the energy flows of the microgrid even under various transitions.Conference Object Citation - WoS: 2Micro-Grid Campus Concept from Data to Design: Case Study Malta(IEEE, 2020-06-14) Azzopardi, Brian; Azzopardi, Stefan; Bartolo, Brian; Jately, Vibhu; Mikalauskine, Renata; Bhattacharya, Somesh; Camilleri, TimThis paper aims to highlight the endeavors of a micro-grid campus development from data to design stage that is under development at the Malta College of Arts, Science and Technology (MCAST), Malta. Malta is an island in the middle of the Mediterranean Sea having an area of 316km2 and receives the highest EU solar irradiance. The MCAST micro-grid is the first living laboratory for training and research on the island with one-third of the campus fully development in state-of-the-art facilities. In this case study, the loads consumption, photovoltaic (PV) generation and potential Electric Vehicles (EVs), that may support the campus when necessary are analysed for further designs supported by over 2 years of campus data. This analysis would provide the understanding of integrating future EVs on campus and higher penetration of PVs while keeping high consumption loads at watch. In addition, reliability and cost factors of the MCAST micro-grid are considered and recommendations are given on the infrastructure to complete campus wide transformation.Conference Object Citation - WoS: 22Citation - Scopus: 25Designing and Modelling Selective Replication for Fault-Tolerant HPC Applications(IEEE, 2017-05) Subasi, Omer; Yalcin, Gulay; Zyulkyarov, Ferad; Unsal, Osman; Labarta, JesusFail-stop errors and Silent Data Corruptions (SDCs) are the most common failure modes for High Performance Computing (HPC) applications. There are studies that address fail-stop errors and studies that address SDCs. However few studies address both types of errors together. In this paper we propose a software-based selective replication technique for HPC applications for both fail-stop errors and SDCs. Since complete replication of applications can be costly in terms of resources, we develop a runtime-based technique for selective replication. Selective replication provides an opportunity to meet HPC reliability targets while decreasing resource costs. Our technique is low-overhead, automatic and completely transparent to the user.Conference Object Citation - WoS: 6Citation - Scopus: 7A Runtime Heuristic to Selectively Replicate Tasks for Application-Specific Reliability Targets(IEEE, 2016-09) Subasi, Omer; Yalcin, Gulay; Zyulkyarov, Ferad; Unsal, Osman; Labarta, JesusIn this paper we propose a runtime-based selective task replication technique for task-parallel high performance computing applications. Our selective task replication technique is automatic and does not require modification/recompilation of OS, compiler or application code. Our heuristic, we call App_FIT, selects tasks to replicate such that the specified reliability target for an application is achieved. In our experimental evaluation, we show that App_FIT selective replication heuristic is low-overhead and highly scalable. In addition, results indicate that complete task replication is overkill for achieving reliability targets. We show that with App_FIT, we can tolerate pessimistic exascale error rates with only 53% of the tasks being replicated.