The ongoing electrification of the transportation fleet will increase the load on the electric power grid. Since both the transportation network and the power grid already experience periods of significant stress, joint analyses of both infrastructures will most likely be necessary to ensure acceptable operation in the future. To enable such analyses, this paper presents an open- source testbed that jointly simulates high-fidelity models of both the electric distribution system and the transportation network. The testbed utilizes two open-source simulators, OpenDSS to simulate the electric distribution system and the microscopic traffic simulator SUMO to simulate the traffic dynamics. Electric vehicle charging links the electric distribution system and the transportation network models at vehicle locations determined using publicly available parcel data. Leveraging high-fidelity synthetic electric distribution system data from the SMART-DS project and transportation system data from OpenStreetMap, this testbed models the city of Greensboro, NC down to the household level. Moreover, the methodology and the supporting scripts released with the testbed allow adaption to other areas where high-fidelity geolocated OpenDSS datasets are available. After describing the components and usage of the testbed, we exemplify applications enabled by the testbed via two scenarios modeling the extreme stresses encountered during evacuations.
more »
« less
SDCWorks: A Formal Framework for Software Defined Control of Smart Manufacturing Systems
Discrete manufacturing systems are complex cyber-physical systems (CPS) and their availability, performance, and quality have a big impact on the economy. Smart manufacturing promises to improve these aspects. One key approach that is being pursued in this context is the creation of centralized software-defined control (SDC) architectures and strategies that use diverse sensors and data sources to make manufacturing more adaptive, resilient, and programmable. In this paper, we present SDCWorks-a modeling and simulation framework for SDC. It consists of the semantic structures for creating models, a baseline controller, and an open source implementation of a discrete event simulator for SDCWorks models. We provide the semantics of such a manufacturing system in terms of a discrete transition system which sets up the platform for future research in a new class of problems in formal verification, synthesis, and monitoring. We illustrate the expressive power of SDCWorks by modeling the realistic SMART manufacturing testbed of University of Michigan. We show how our open source SDCWorks simulator can be used to evaluate relevant metrics (throughput, latency, and load) for example manufacturing systems.
more »
« less
- Award ID(s):
- 1544901
- NSF-PAR ID:
- 10313894
- Date Published:
- Journal Name:
- ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)This paper introduces Simulus, a full-fledged open-source discrete-event simulator, supporting both event-driven and process-oriented simulation world-views. Simulus is implemented in Python and aspires to be a part of the Python's ecosystem supporting scientific computing. Simulus also provides several advanced modeling constructs to ease common simulation tasks (e.g., complex queuing models, interprocess synchronizations, and message-passing communications). Simulus also provides organic support for simultaneously running a time-synchronized group of simulators, either sequentially or in parallel, thereby allowing composable simulation of individual simulators handling different aspects of a target system, and enabling large-scale simulation running on parallel computers. This paper describes the salient features of Simulus and examines its major design decisions.more » « less
-
Abstract The recent COVID-19 pandemic reveals the vulnerability of global supply chains: the unforeseen supply crunches and unpredictable variability in customer demands lead to catastrophic disruption to production planning and management, causing wild swings in productivity for most manufacturing systems. Therefore, a smart and resilient manufacturing system (S&RMS) is promised to withstand such unexpected perturbations and adjust promptly to mitigate their impacts on the system’s stability. However, modeling the system’s resilience to the impacts of disruptive events has not been fully addressed. We investigate a generalized polynomial chaos (gPC) expansion-based discrete-event dynamic system (DEDS) model to capture uncertainties and irregularly disruptive events for manufacturing systems. The analytic approach allows a real-time optimization for production planning to mitigate the impacts of intermittent disruptive events (e.g., supply shortages) and enhance the system’s resilience. The case study on a hybrid bearing manufacturing workshop suggests that the proposed approach allows a timely intervention in production planning to significantly reduce the downtime (around one-fifth of the downtime compared to the one without controls) while guaranteeing maximum productivity under the system perturbations and uncertainties.more » « less
-
more » « less
Abstract Summary A major barrier to the practical utilization of large, complex models of biochemical systems is the lack of open-source computational tools to evaluate model behaviors over high-dimensional parameter spaces. This is due to the high computational expense of performing thousands to millions of model simulations required for statistical analysis. To address this need, we have implemented a user-friendly interface between cupSODA, a GPU-powered kinetic simulator, and PySB, a Python-based modeling and simulation framework. For three example models of varying size, we show that for large numbers of simulations PySB/cupSODA achieves order-of-magnitude speedups relative to a CPU-based ordinary differential equation integrator.
Availability and implementation The PySB/cupSODA interface has been integrated into the PySB modeling framework (version 1.4.0), which can be installed from the Python Package Index (PyPI) using a Python package manager such as pip. cupSODA source code and precompiled binaries (Linux, Mac OS/X, Windows) are available at github.com/aresio/cupSODA (requires an Nvidia GPU; developer.nvidia.com/cuda-gpus). Additional information about PySB is available at pysb.org.
Supplementary information Supplementary data are available at Bioinformatics online.
-
null (Ed.)The emergence and steady adoption of machine communication protocols like the MTConnect are steering the manufacturing sector towards greater machine interoperability, higher operational productivity, substantial cost savings with advanced decision-making capabilities at the shop-floor level. MTConnect GitHub repository and NIST Smart Manufacturing Systems (SMS) Test Bed are two major resources for collecting data from CNC machines. However, these tools would be insufficient and protractive in Modeling & Simulation (M&S) scenarios where spawning hundreds of MTConnect agents and thousands of adapters with real-time virtual machining is necessary for advancing research in the digital supply chain. This paper introduces a flexible simulator testbed of multiple MTConnect agents and adapters for simulating Levels 0 & 1 of the ISA-95 framework and help support R&D activities in complex multi-enterprise supply chain scenarios. To the best knowledge of the authors, there is no publicly accessible multi-enterprise MTConnect testbed yet.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCPS.2018.00017
