The NIST Transactive Energy (TE) Modeling and Simulation Challenge for the Smart Grid (Challenge) spanned from 2015 to 2018. The TE Challenge was initiated to identify simulation tools and expertise that might be developed or combined in co-simulation platforms to enable the evaluation of transactive energy approaches. Phase I of the Challenge spanned 2015 to 2016, with team efforts that improved understanding of TE concepts, identified relevant simulation tools and co-simulation platforms, and inspired the development of a TE co-simulation abstract component model that paved the way for Phase II. The Phase II effort spanned Spring 2017 through Spring 2018, where the teams collaboratively developed a specific TE problem scenario, a common grid topology, and common reporting metrics to enable direct comparison of results from simulation of each team's TE approach for the defined scenario. This report presents an overview of the TE Challenge, the TE abstract component model, and the common scenario. It also compiles the individual Challenge participants' research reports from Phase II. The common scenario involves a weather event impacting a distribution grid with very high penetration of photovoltaics, leading to voltage regulation challenges that are to be mitigated by TE methods. Four teams worked with this common scenario and different TE models to incentivize distributed resource response to voltage deviations, performing these simulations on different simulation platforms. A fifth team focused on a co-simulation platform that can be used for online TE simulations with existing co-simulation components. The TE Challenge Phase II has advanced co-simulation modeling tools and platforms for TE system performance analysis, developed a referenceable TE scenario that can support ongoing comparative simulations, and demonstrated various TE approaches for managing voltage on a distribution grid with high penetration of photovoltaics.

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Short term load forecasting for day ahead operations is an important task of an electric distribution company. Forecasting errors directly impact the economics of the distribution company in a market scenario. Many categories of methods like, expert system, artificial neural network and time series analysis, have been developed for short term load forecasting. We compare and contrast these methods on a utility data set. It is seen that no method can be said to be consistently better or worse than the other. Therefore, this paper explores the idea of development of a combination forecast from the three individual forecasts. The combination forecast is shown to have better expected performance than any one of the individual forecasts. Different methods of combining forecasts like, in proportion to probability of success, combining with weights calculated from variance minimization, on the basis of eigenvector of covariance matrix and median forecast are considered. Results of an urban electric distribution company's data is used to demonstrate the efficacy of the approaches.