In the last few years, offshore wind power has experienced remarkable growth attributable to the need to mitigate the impact of global warming, which leads to policies for reducing greenhouse gas emissions and fostering investment in renewable technologies. However, this endeavor raises concerns regarding economic assessment studies, where the levelized cost of energy (LCOE) is the most widely adopted index. This paper aims to propose an estimation of the LCOE under generation uncertainty induced by the intermittency of the wind and thus determine how the LCOE obtained by a deterministic approach is affected. To carry out this task, we employ two techniques based on stochastic differential equations to generate wind power scenarios. Utilizing 40 years of recorded wind speed data sourced from the MERRA-2 reanalysis, we derive the input parameters of the models from fitting to 4-parameter Dagum and Johnson SB probability distributions, which shows that accurate modeling of offshore wind speeds requires dealing with complex mathematical functions that need numerical computation. The findings show that the stochastic approach can estimate an uncertainty interval around the result obtained via the deterministic approach. In the case studies examined, the maximum and minimum values correspond to approximately ±1.6% and ±3.5% from the deterministic LCOE.

There is considerable interest in broadband nanomaterials, particularly transparent semiconductor oxides, within both fundamental research and technological applications. Historically, it has been considered that the variation in dopant concentration during the synthesis of semiconductor materials is a crucial factor in activating and/or modulating the optical and structural properties, particularly the bandgap and the parameters of the unit cell, of semiconductor oxides. Recently, tin oxide has emerged as a key material due to its excellent structural properties, optical transparency, and various promising applications in optoelectronics. This study utilized the ultrasonic spray pyrolysis technique to synthesize aluminum-doped tin oxide (ATO) thin films on quartz and polished single-crystal silicon substrates. The impact of varying aluminum doping levels (0, 2, 5, and 10 at. %) on morphology and structural and optical properties was examined. The ATO thin films were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), and transmittance spectroscopy. SEM images demonstrated a slight reduction in the size of ATO nanoparticles as the aluminum doping concentration increased. XRD analysis revealed a tetragonal crystalline structure with the space group P42/mnm, and a shift in the XRD peaks to higher angles was noted with increasing aluminum content, indicating a decrease in the crystalline lattice parameters of ATO. The transmittance of the ATO films varied between 75% and 85%. By employing the transmittance spectra and the established Tauc formula the optical bandgap values of ATO films were calculated, showing an increase in the bandgap with higher doping levels. These findings were thoroughly analyzed and discussed; additionally, an effort was made to clarify the contradictory analyses present in the literature and to identify a doping range that avoids the onset of a secondary phase.

In this paper we consider the problem of an insurance company where the wealth of the insurer is described by a Cramér-Lundberg process. The insurer is allowed to invest in a risky asset with stochastic volatility subject to the influence of an economic factor and the remaining surplus in a bank account. The price of the risky asset and the economic factor are modeled by a system of correlated stochastic differential equations. In a finite horizon framework and assuming that the market is incomplete, we study the problem of maximizing the expected utility of terminal wealth. When the insurer’s preferences are exponential, an existence and uniqueness theorem is proven for the non-linear Hamilton-Jacobi-Bellman equation (HJB). The optimal strategy and the value function have been produced in closed form. In addition and in order to show the connection between the insurer’s decision and the correlation coefficient we present two numerical approaches: A Monte-Carlo method based on the stochastic representation of the solution of the insurer problem via Feynman-Kac’s formula, and a mixed Finite Difference Monte-Carlo one. Finally the results are presented in the case of Scott model.

In this article, we present a modification to the Economic Dispatch (ED) model that addresses the non-convex nature of the cost curves associated with a Combined Cycle Power Plant (CCPP). Incorporating a binary variable provides greater precision in solving the combinatorial problem in only one simulation and, most importantly, demonstrates cost minimization among the three different cost curve models for dispatching the CCPP. Our results highlight the importance of considering different demand scenarios based on a reference forecast for one day ahead. Therefore, piecewise modeling is more feasible for solving the non-convex problem, showing greater accuracy regarding the operational state of the CCPP and avoiding the cost overestimation that occurs with traditional models. Moreover, it allows the operators to manage costs better and optimize generation potential, ultimately showing economic benefits for the system operator.