scholarly journals Autonomous Microgrid Design Using Classifier-Guided Sampling

2015 ◽  
Author(s):  
Peter B. Backlund ◽  
John P. Eddy
Keyword(s):  
High Performance ◽  
Discrete Event ◽  
Hurricane Sandy ◽  
System Level ◽  
Design Problems ◽  
Event Simulation ◽  
Performance System ◽  
Performance Statistics ◽  
The City ◽  
Microgrid Design

Identifying high-performance, system-level microgrid designs is a significant challenge due to the overwhelming array of possible configurations. Uncertainty relating to loads, utility outages, renewable generation, and fossil generator reliability further complicates this design problem. In this paper, the performance of a candidate microgrid design is assessed by running a discrete event simulation that includes extended, unplanned utility outages during which microgrid performance statistics are computed. Uncertainty is addressed by simulating long operating times and computing average performance over many stochastic outage scenarios. Classifier-guided sampling, a Bayesian classifier-based optimization algorithm for computationally expensive design problems, is used to search and identify configurations that result in reduced average load not served while not exceeding a predetermined microgrid construction cost. The city of Hoboken, NJ, which sustained a severe outage following Hurricane Sandy in October, 2012, is used as an example of a location in which a well-designed microgrid could be of great benefit during an extended, unplanned utility outage. The optimization results illuminate design trends and provide insights into the traits of high-performance configurations.

scholarly journals Optimization of the Heap Leaching Process through Changes in Modes of Operation and Discrete Event Simulation

Minerals ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 421 ◽  
Author(s):  
Manuel Saldaña ◽  
Norman Toro ◽  
Jonathan Castillo ◽  
Pía Hernández ◽  
Alessandro Navarra
Keyword(s):  
Discrete Event Simulation ◽  
High Performance ◽  
Discrete Event ◽  
Mineral Resources ◽  
Mineralogical Composition ◽  
Heap Leaching ◽  
Mine Planning ◽  
Modes Of Operation ◽  
Event Simulation ◽  
Productive Phase

The importance of mine planning is often underestimated. Nonetheless, it is essential in achieving high performance by identifying the potential value of mineral resources and providing an optimal, practical, and realistic strategy for extraction, which considers the greatest quantity of options, materials, and scenarios. Conventional mine planning is based on a mostly deterministic approach, ignoring part of the uncertainty presented in the input data, such as the mineralogical composition of the feed. This work develops a methodology to optimize the mineral recovery of the heap leaching phase by addressing the mineralogical variation of the feed, by alternating the mode of operation depending on the type of ore in the feed. The operational changes considered in the analysis include the leaching of oxide ores by adding only sulfuric acid (H2SO4) as reagent and adding chloride in the case of sulfide ores (secondary sulfides). The incorporation of uncertainty allows the creation of models that maximize the productivity, while confronting the geological uncertainty, as the extraction program progresses. The model seeks to increase the expected recovery from leaching, considering a set of equiprobable geological scenarios. The modeling and simulation of this productive phase is developed through a discrete event simulation (DES) framework. The results of the simulation indicate the potential to address the dynamics of feed variation through the implementation of alternating modes of operation.

Managing funerary systems in the pandemic: lessons learned and an application of a scenario simulation in São Paulo City, Brazil

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Irineu de Brito Jr ◽  
Manoel Henrique Capistrano Cunha ◽  
Luiz Antonio Tozi ◽  
Luiz Augusto Franzese ◽  
Márcia Lorena da Silva Frazão ◽  
...  
Keyword(s):  
Decision Making ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Sao Paulo ◽  
Lessons Learned ◽  
São Paulo ◽  
Content Type ◽  
Event Simulation ◽  
Discrete Event Simulation Model ◽  
The City

PurposeThis study, a practice forum article, aims to presents the lessons learned and the development of a discrete event simulation model to support the funerary system management of São Paulo City, Brazil, during the COVID-19 pandemic.Design/methodology/approachA discrete event simulation model was developed by the authors as soon as the pandemic affected the city of São Paulo, Brazil. Based on the model, several scenarios with varying minimum, median and peak demands (i.e. the number of deaths) were tested and evaluated. The lessons learned from the scenario analysis and implementation of the decision-making of the city government of São Paulo are discussed in this article.FindingsThe lessons learned about the coordination, inventory management and other operational characteristics in funerary logistics during the pandemic are shared with a model, which quantifies the demand for vehicles, coffins, graves and teams in the cemeteries in different simulated scenarios.Practical implicationsThe São Paulo State Civil Defense used this information during the pandemic to prepare the funerary system of the municipality.Social implicationsThe study presents methods to mitigate the sanitary, environmental and psychosocial problems related to the funerary system.Originality/valueStudies on funerary systems are scarce. This study presents the results that supported the dimensioning of the funerary system during the pandemic and operational lessons about the logistics to support decision-making in future events.

scholarly journals ABSTRACTION CHECKPOINTING LEVELS: PROBLEMS AND SOLUTIONS

2014 ◽  
pp. 158-169
Author(s):  
Bakhta Meroufel ◽  
Ghalem Belalem
Keyword(s):  
Fault Tolerance ◽  
Discrete Event Simulation ◽  
Scientific Computing ◽  
Discrete Event ◽  
Acceptable Level ◽  
System Level ◽  
Event Simulation ◽  
Abstraction Level ◽  
Problems And Solutions ◽  
The Difference

A common approach to guarantee an acceptable level of fault tolerance in scientific computing is the checkpointing. In this strategy: when a task fails, it is allowed to be restarted from the recently checked pointed state rather than from the beginning, which reduces the system loss and ensures the reliability. Several systems use the checkpointing to ensure the fault tolerance such as HPC, distributed discrete event simulation and Clouds. The literature proposes several classifications of checkpointing techniques using different metrics and criteria. In this paper we focus on the classification based on abstraction level. In this classification the checkpointing is categorized into two principal types: application level and system level. Each of these levels has its advantages and suffers from many problems. The difference between our present paper and the others surveys proposed in the literature is that: in this paper we will study each level in details. We will also study and analyze some works that propose solutions to solve the problems and exceed the limits of each abstraction level.

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