Global atlas of solar and wind resources temporal complementarity

The concept of renewable energy sources complementarity has attracted the attention of researchers across the globe over recent years. Studies have been published regularly with focuses on aspects such as new metrics for complementarity assessment, the optimal operation of hybrid power systems based on variable renewables, or mapping resources complementarity in a specific region. This study targets the present literature gap, namely a lack of complementarity study covering explicitly the whole World, based on the same data source and methodology. The research employs Kendall's Tau correlation as the complementarity metric between global solar and wind resources and a pair of indicators such as the solar share and a sizing coefficient usually applied in the domain of hybrid generators. This method allows to conduct a preliminary estimation of a solar and wind energy hybrid generator based on a daily demand of 1 kWh. The data series employed in this study come from NASA’s POWER Project Program, covering the years 2001–2020. This work provides an interesting insight into the global variability of the complementarity between these two variable energy sources. Significant findings of this paper include that Kendall’s Tau ranges between –0.75 and 0.75, in line with previous research for specific regions, thus providing a theoretical maximum for planning. Additionally, the results suggest that in most tropical and subtropical areas, the hybrid solar-wind generator should be dominated by the solar portion to minimize the variability of the total daily energy produced

Global atlas of solar and wind resources temporal complementarity

The concept of renewable energy sources complementarity has attracted the attention of researchers across the globe over recent years. Studies have been published regularly with focuses on aspects such as new metrics for complementarity assessment, the optimal operation of hybrid power systems based on variable renewables, or mapping resources complementarity in a specific region. This study targets the present literature gap, namely a lack of complementarity study covering explicitly the whole World, based on the same data source and methodology. The research employs Kendall’s Tau correlation as the complementarity metric between global solar and wind resources and a pair of indicators such as the solar share and a sizing coefficient usually applied in the domain of hybrid generators. This method allows to conduct a preliminary estimation of a solar and wind energy hybrid generator based on a daily demand of 1 kWh. The data series employed in this study come from NASA’s POWER Project Program, covering the years 2001–2020. This work provides an interesting insight into the global variability of the complementarity between these two variable energy sources. Significant findings of this paper include that Kendall’s Tau ranges between –0.75 and 0.75, in line with previous research for specific regions, thus providing a theoretical maximum for planning. Additionally, the results suggest that in most tropical and subtropical areas, the hybrid solar-wind generator should be dominated by the solar portion to minimize the variability of the total daily energy produced

Estimation of fuel consumption in a hybrid electric refuse collector vehicle using a real drive cycle

Postprint (published version

Motion Planning of Uncertain Ordinary Differential Equation Systems

This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems. Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs. The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space

Blind deconvolution of medical ultrasound images: parametric inverse filtering approach

©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used