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The article of record as published may be found at http://dx.doi.org/10.1016/j.apenergy.2017.05.068 ; Hybrid microgrids that use renewable energy sources can improve energy security and islanding time while reducing costs. One potential beneficiary of these systems is the U.S. military, which can seek to improve energy security when operating in isolated areas by using a microgrid rather than relying on a fragile (or nonexistent) commercial network. Renewable energy sources can be intermittent and unpredictable, making it difficult to plan operations of a microgrid. We describe a scenario-robust mixedinteger linear program designed to utilize ensemble weather forecasts to improve the performance of a hybrid microgrid containing both renewable and traditional power sources. We exercise our model to quantify the benefit of using ensemble weather forecasts, and we predict the optimal performance of a hypothetical grid containing wind turbines by using simulated realistic weather forecast scenarios based on data. Because forecast quality degrades with lead time, we perform a sensitivity analysis to determine which planning horizon results in the best performance. Our results show that, for dayahead planning, longer planning horizons outperform shorter planning horizons in terms of cost of operations, but this improvement diminishes as the planning horizon lengthens. ; Office of Naval Research

The article of record may be found at http://dx.doi.org/10.1007/s12667-015-0142-z ; In an effort to reduce carbon dioxide (CO2) emissions to the atmosphere, carbon capture and storage (CCS) technology has been developed to collect CO2 from emissions generators and store it underground. Recent proposed legislation would limit the volume of emissions generated from power sources, effectively requiring some sources to participate in CCS. Both emissions sources and storage operators require incentives to enter into contracts to capture excess emissions at the source, and transport and store the CO2 underground. As the level of emissions from power plants is stochastic and carryover into future time periods is expensive, we develop a newsvendor model to determine the optimal price and volume of these contracts to maximize the expected profit of the storage operator and encourage the participation of multiple emissions sources. Because the storage operator has a limit on the amount of CO2 that can be injected each month, this limit affects the allocation of the optimal contract amounts between the emitters. The distribution of emissions and relative costs of transportation also influence the optimal policy. In addition to analytical solutions, we present data-driven methods for using correlated emissions data to determine the optimal price and volume of these contracts.

The article of record as published may be found at http://dx.doi.org/10.1080/01605682.2019.1570806 ; We study the problem of modelling the trajectory of a moving object of interest, or target, given limited locational and temporal information. Because of uncertainty in information, the location of the target can be represented using a spatial distribution, or heatmap. This paper proposes a comprehensive method for constructing and updating probability heatmaps for the location of a moving object based on uncertain information. This method uses Brownian bridges to model and construct temporal probability heatmaps of target movement, and employs a particle filter to update the heatmap as new intelligence arrives. This approach allows for more complexity than simple deterministic motion models, and is computationally easier to implement than detailed models for local target movement. ; Center for Multi-Intelligence Studies at the Naval Postgraduate School

The article of record as published may be found at http://dx.doi.org/10.1109/WSC.2014.7019964 ; Hybrid microgrids containing renewable energy sources represent a promising option for organizations wishing to reduce costs while increasing energy security and islanding time. A prime example of such an organization is the U.S. military, which often operates in isolated areas and whose reliance on a fragile commercial electric grid is seen as a security risk. However, incorporating renewable sources into a microgrid is difficult due to their typically intermittent and unpredictable nature. We use simulation techniques to investigate the performance of a hypothetical hybrid microgrid containing both wind turbines and fossil fuel based power sources. Our simulation model produces realistic weather forecast scenarios, which we use to exercise our optimization model and predict optimal grid performance. We perform a sensitivity analysis and find that for day-ahead planning, longer planning horizons are superior to shorter planning horizons, but this improvement diminishes as the length of the planning horizon increases.