This study presents an open-source software platform specifically tailored to permit careful dynamic performance evaluation of transactive energy designs for end-to-end electric power systems. The platform models a centrally-managed wholesale power market operating over a transmission grid linked to one or more distribution systems, where each distribution system consists of a collection of grid-edge resources operating over a distribution grid. Test case findings are presented to illustrate the capabilities of the platform. The test cases implement a transmission system linked to a distribution system populated by households that have smart price-responsive appliances as well as conventional loads. Transactions at the distribution level are conducted in accordance with a well-known bid-based transactive energy design known as the PowerMatcher. AbstractThis study presents an open-source software platform specifically tailored to permit careful dynamic performance evaluation of transactive energy designs for end-to-end electric power systems. The platform models a centrallymanaged wholesale power market operating over a transmission grid linked to one or more distribution systems, where each distribution system consists of a collection of grid-edge resources operating over a distribution grid. Test case findings are presented to illustrate the capabilities of the platform. The test cases implement a transmission system linked to a distribution system populated by households that have smart price-responsive appliances as well as conventional loads. Transactions at the distribution level are conducted in accordance with a well-known bid-based transactive energy design known as the PowerMatcher.

Clustering algorithms have been explored in recent years to solve hotspot clustering problems in integrated circuit design. With various applications in design for manufacturability flow such as hotspot library generation, systematic yield optimization, and design space exploration, generating good quality clusters along with their representative clips is of utmost importance. With several generic clustering algorithms at our disposal, hotspots can be clustered based on the distance metric defined while satisfying some tolerance conditions. However, the clusters generated from generic clustering algorithms need not achieve optimal results. In this paper, we introduce two optimal integer linear programming formulations based on triangle inequality to solve the problem of minimizing cluster count while satisfying given constraints. Apart from minimizing cluster count, we generate representative clips that best represent the clusters formed. We achieve a better cluster count for both formulations in most test cases as compared to the results published in the literature in the ICCAD 2016 contest benchmarks as well as the reference results reported in the ICCAD 2016 contest website.

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