This paper describes GridSpice, a scalable opensource simulation framework for modeling, designing, and planning of the smart grid. GridSpice seamlessly integrates existing electric power simulation tools to enable modeling of large electric networks that blur the boundaries between generation, transmission, distribution, and markets. This is achieved via a cloud-based architecture that allows for parallelizing large simulation jobs across many virtual machines using a pay-as-you-go model. Grid-Spice simulations can be managed through a Representational State Transfer (REST) application programming interface (API), or through a Python library, allowing users to run simulations programmatically and interface with disparate data inputs, energy management systems (EMS), distribution management systems (DMS), and postprocessing tools. These capabilities make GridSpice an ideal tool for the development and testing of new grid control and optimization algorithms. GridSpice also provides an easy-to-use browser-based interface to allow novice users to begin without any setup or configuration on their local PC. A first implementation of the GridSpice framework integrates Gridlab-D and MATPOWER as simulation tools, and has been used for projects including optimizing the placement of distributed generation and developing optimal dispatch schedules for flexible loads. The GridSpice framework and Gridlab-D are freely available in open-source under the BSD license.
This paper describes GridSpice, a scalable opensource simulation framework for modeling, designing, and planning of the smart grid. GridSpice seamlessly integrates existing electric power simulation tools to enable modeling of large electric networks that blur the boundaries between generation, transmission, distribution, and markets. This is achieved via a cloud-based architecture that allows for parallelizing large simulation jobs across many virtual machines using a pay-as-you-go model. Grid-Spice simulations can be managed through a Representational State Transfer (REST) application programming interface (API), or through a Python library, allowing users to run simulations programmatically and interface with disparate data inputs, energy management systems (EMS), distribution management systems (DMS), and postprocessing tools. These capabilities make GridSpice an ideal tool for the development and testing of new grid control and optimization algorithms. GridSpice also provides an easy-to-use browser-based interface to allow novice users to begin without any setup or configuration on their local PC. A first implementation of the GridSpice framework integrates Gridlab-D and MATPOWER as simulation tools, and has been used for projects including optimizing the placement of distributed generation and developing optimal dispatch schedules for flexible loads. The GridSpice framework and Gridlab-D are freely available in open-source under the BSD license.
In this paper, we address the problem of memory-efficient construction of ROBDDs for a given Boolean network. We show that for a large number of applications, it is more efficient to construct the ROBDD by a suitable combination of top-down and bottom-up approaches than a purely bottom-up approach. We first build a decomposed ROBDD of the target function and then follow it by a symbolic composition to get the final ROBDD. We propose two heuristic algorithms for decomposition. One is based on a topological analysis of the given netlist, while the other is purely functional, making no assumptions about the underlying circuit topology. We demonstrate the utility of our methods on standard benchmark circuits as well as some hard industrial circuits. Our results show that this method requires significantly less memory than the conventional bottom-up construction. In many cases, we are able to build the ROBDDs of outputs for which the conventional method fails. In addition, in most cases this memory reduction is accompanied by a significant speed up in the ROBDD construction process.
W i t h the increase in the complexity of present day systems, proving the correctness of a design has become a major concern. Simulation based methodologies are generally inadequate to validate the correctness of a design with a reasonable confidence. More and more designers are moving towards formal methods to guarantee the correctness of their designs. I n this paper we survey some state-of-the-art techniques used to perjorm automatic verification of combinational eircaits.We classify the current approaches f o r combinational verification into two categories: functional and structural. The functional methods consist of representing a circuit as a canonical decision diagram. Two circuits are equivalent i f and only if their decision diagrams are equal. The structural methods consist of identifying related nodes in the circuit and using them to simplify the problem of verification. We briefly describe s o m e of the methods in both the categories and discuss their merits and drawbacks.
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