Using microscopic and molecular techniques combined with computational analysis, this study examined the structure and composition of microbial communities in biofilms that formed on different artificial substrates in a brine pool and on a seep vent of a cold seep in the Red Sea to test our hypothesis that initiation of the biofilm formation and spreading mode of microbial structures differs between the cold seep and the other aquatic environments. Biofilms on different substrates at two deployment sites differed morphologically, with the vent biofilms having higher microbial abundance and better structural features than the pool biofilms. Microbes in the pool biofilms were more taxonomically diverse and mainly composed of various sulfate-reducing bacteria whereas the vent biofilms were exclusively dominated by sulfur-oxidizing Thiomicrospira. These results suggest that the redox environments at the deployment sites might have exerted a strong selection on microbes in the biofilms at two sites whereas the types of substrates had limited effects on the biofilm development.
Forecast errors can cause sub-optimal solutions in resource planning optimization, yet they are usually modeled simplistically by statistical models, causing unrealistic impacts on the optimal solutions. In this paper, realistic forecast errors are prescribed, and a corrective approach is proposed to mitigate the negative effects of day-ahead persistence forecast error by short-term forecasts from a state-of-the-art sky imager system. These forecasts preserve the spatiotemporal dependence structure of forecast errors avoiding statistical approximations. The performance of the proposed algorithm is tested on a receding horizon quadratic program developed for valley filling the midday net load depression through electric vehicle charging. Throughout one month of simulations the ability to flatten net load is assessed under practical forecast accuracy levels achievable from persistence, sky imager and perfect forecasts. Compared to using day-ahead persistence solar forecasts, the proposed corrective approach using sky imager forecasts delivers a 25% reduction in the standard deviation of the daily net load. It is demonstrated that correcting day-ahead forecasts in real time with more accurate short-term forecasts benefits the valley filling solution.
Cloud base height (CBH) is an important parameter for physics-based high resolution solar radiation modeling. In sky imager-based forecasts, a ceilometer or stereographic setup is needed to derive the CBH; otherwise erroneous CBHs lead to incorrect physical cloud velocity and incorrect projection of cloud shadows, causing solar power forecast errors due to incorrect shadow positions and timing of shadowing events. In this paper, two methods to estimate cloud base height from a single sky imager and distributed ground solar irradiance measurements are proposed. The first method (Time Series Correlation, denoted as "TSC") is based upon the correlation between ground-observed global horizontal irradiance (GHI) time series and a modeled GHI time series generated from a sequence of sky images geo-rectified to a candidate set of CBH. The estimated CBH is taken as the candidate that produces the highest correlation coefficient. The second method (Geometric Cloud Shadow Edge, denoted as "GCSE") integrates a numerical ramp detection method for ground-observed GHI time series with solar and cloud geometry applied to cloud edges in a sky image. CBH are benchmarked against a collocated ceilometer and stereographically estimated CBH from two sky imagers for 15 minute medianfiltered CBHs. Over 30 days covering all seasons, the TSC method performs similarly to the GCSE method with nRMSD of 18.9% versus 20.8%. A key limitation of both proposed methods is the requirement of sufficient variation in GHI to enable reliable correlation and ramp detection. The advantage of the two proposed methods is that they can be applied when measurements from only a single sky imager and pyranometers are available.
Large PV power ramp rates are of concern and sometimes even explicitly restricted by grid operators. Battery energy storage systems can smooth the power output and maintain ramp rates within permissible limits. To enable PV plant and energy storage systems design and planning, a method to estimate the largest expected ramps for a given location is proposed. Because clouds are the dominant source of PV power output variability, an analytical relationship between the worst expected ramp rates, cloud motion vectors, and the geometrical layout of the PV plant is developed. The ability of the proposed method to bracket actual ramp rates is assessed over 8 months under different meteorological conditions, demonstrating an average compliance rate of 96.9% for a 2 min evaluation time window.
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