• Energy Research

The wind and wave climatology of the Red Sea is derived from a validated 30-yr high-resolution model simulation. After describing the relevant features of the basin, the main wind and wave systems are identified by using an innovative spectral partition technique to explain their genesis and characteristics. In the northern part of the sea, wind and waves of the same intensity are present throughout the year, while the central and southern zones are characterized by a marked seasonality. The partition technique allows the association of a general decrease in the energy of the different wave systems with a specific weather pattern. The most intense decrease is found in the northern storms, which are associated with meteorological pulses from the Mediterranean Sea.

AbstractIn the tropics, thermal stratification (during warm conditions) may contribute to a shallowing of the mixed layer above the nutricline and a reduction in the transfer of nutrients to the surface lit-layer, ultimately limiting phytoplankton growth. Using remotely sensed observations and modelled datasets, we study such linkages in the northern Red Sea (NRS) - a typical tropical marine ecosystem. We assess the interannual variability (1998–2015) of both phytoplankton biomass and phenological indices (timing of bloom initiation, duration and termination) in relation to regional warming. We demonstrate that warmer conditions in the NRS are associated with substantially weaker winter phytoplankton blooms, which initiate later, terminate earlier and are shorter in their overall duration (~ 4 weeks). These alterations are directly linked with the strength of atmospheric forcing (air-sea heat fluxes) and vertical stratification (mixed layer depth [MLD]). The interannual variability of sea surface temperature (SST) is found to be a good indicator of phytoplankton abundance, but appears to be less important for predicting bloom timing. These findings suggest that future climate warming scenarios may have a two-fold impact on phytoplankton growth in tropical marine ecosystems: 1) a reduction in phytoplankton abundance and 2) alterations in the timing of seasonal phytoplankton blooms.

Abstract In tropical oceans, phytoplankton experience significant alterations during marine heatwaves (MHWs), yet the consequences of reduced or absent marine cold-spells (MCSs) on these microscopic algae are currently overlooked. Synergistically combining in situ measurements, Argo-float data, remotely-sensed observations, and hydrodynamic model outputs, we explore such relationships in the Red Sea. Results show a long-term (1982 to 2018) gradual increase in MHW days (5–20 days/decade) and a clear decrease in MCS days (10–30 days/decade). Compound extreme temperature and chlorophyll-a events (Chl-a – an index of phytoplankton biomass) exhibit consistently lower Chl-a concentrations during MHWs and higher ones during MCSs, particularly in the northern and southern Red Sea. In these regions, during the main phytoplankton-growth period, the presence of MHWs/MCSs leads to respective Chl-a anomalies in 94% of the cases. Yet, phytoplankton responses in the central Red Sea are more complex, most likely linked to the region’s highly dynamic circulation (e.g., mesoscale anti-cyclonic eddies), and multiple nutrient sources. In the naturally warm and stratified ecosystem of the Red Sea, where deeper mixed layers enhance the transfer of nutrient-rich waters to the lit zone, the substantial reduction of MCSs could be more impactful for phytoplankton than the gradual rise of MHWs.

Primary production in highly stratified and oligotrophic tropical seas relies primarily on nutrient injections from a deepened mixed layer. The Red Sea, one of the warmest marine ecosystems on earth, has very few external nutrient sources. The role of mixed layer depth (MLD) on phytoplankton dynamics has predominantly been investigated in the northern part of the basin, yet a comprehensive investigation covering the entire basin is currently lacking. By integrating numerical MLD simulations and ocean colour remote sensing observations, both regionally-tuned to the Red Sea environment, the influence of vertical mixing, proxied by the MLD, on chlorophyll-a concentration (CHL) is investigated at seasonal and interannual scales. Results show that the central basin exhibits weak relationships, possibly linked to the intense mesoscale activity and the resulting horizontal advective fluxes. Remarkably, in the southern basin, even minor MLD variations (3%) seem to have a significant response in CHL (~10%). Until now, phytoplankton biomass in the south was linked to the horizontal intrusion of nutrient-rich waters from the Indian Ocean, while our results also stress the importance of vertical mixing in the redistribution of these fertile deeper layer waters to the surface lit zone. Here, we report the diverse role of deepened mixed layers in shaping CHL concentrations across various provinces in the Red Sea.

In this paper, we address the decision-making problem of a virtual power plant (VPP) involving a self-scheduling and market involvement problem under uncertainty in the wind speed and electricity prices. The problem is modeled using a risk-neutral and two risk-averse two-stage stochastic programming formulations, where the conditional value at risk is used to represent risk. A sample average approximation methodology is integrated with an adapted L-Shaped solution method, which can solve risk-neutral and specific risk-averse problems. This methodology provides a framework to understand and quantify the impact of the sample size on the variability of the results. The numerical results include an analysis of the computational performance of the methodology for two case studies, estimators for the bounds of the true optimal solutions of the problems, and an assessment of the quality of the solutions obtained. In particular, numerical experiences indicate that when an adequate sample size is used, the solution obtained is close to the optimal one.

Abstract In this paper, we address the optimal operation of a virtual power plant using stochastic programming. We consider one risk-neutral and two risk-averse formulations that rely on the conditional value at risk. To handle large-scale problems, we implement two decomposition methods with variants using single- and multiple-cuts. We propose the utilization of wind ensembles obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) to quantify the uncertainty of the wind forecast. We present detailed results relative to the computational performance of the risk-averse formulations, the decomposition methods, and risk management and sensitivities analysis as a function of the number of scenarios and risk parameters. The implementation of the two decomposition methods relies on the parallel solution of subproblems, which turns out to be paramount for computational efficiency. The results show that one of the two decomposition methods is the most efficient.

Abstract An ensemble-based history-matching framework is proposed to enhance the characterization of petroleum reservoirs through the assimilation of crosswell electromagnetic (EM) data. As one of advanced technologies in reservoir surveillance, crosswell EM tomography can provide a cross-sectional conductivity map and hence saturation profile at an interwell scale by exploiting the sharp contrast in conductivity between hydrocarbons and saline water. Incorporating this new information into reservoir simulation in combination with other available observations is therefore expected to enhance the forecasting capability of reservoir models and to lead to better quantification of uncertainty. The proposed approach applies ensemble-based data-assimilation methods to build a robust and flexible framework under which various sources of available measurements can be readily integrated. Because the assimilation of crosswell EM data can be implemented in different ways (e.g., components of EM fields or inverted conductivity), a comparative study is conducted. The first approach integrates crosswell EM data in its original form which entails establishing a forward model simulating observed EM responses. In this work, the forward model is based on Archie's law that provides a link between fluid properties and formation conductivity, and Maxwell’s equations that describe how EM fields behave given the spatial distribution of conductivity. Alternatively, formation conductivity can be used for history matching, which is obtained from the original EM data through inversion using an adjoint gradient-based optimization method. Because the inverted conductivity is usually of high dimension and very noisy, an image-oriented distance parameterization utilizing fluid front information is applied aiming to assimilate the conductivity field efficiently and robustly. Numerical experiments for different test cases with increasing complexity are carried out to examine the performance of the proposed integration schemes and potential of crosswell EM data for improving the estimation of relevant model parameters. The results demonstrate the efficiency of the developed history-matching workflow and added value of crosswell EM data in enhancing the characterization of reservoir models and reliability of model forecasts.