Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Parametric CAD model based shape optimization using adjoint functions

Adjoint methods have proven to be an efficient way of calculating the gradient of an objective function with respect to a shape parameter for optimisation, with a computational cost nearly independent of the number of the design variables [1]. The approach in this paper links the adjoint surface sensitivities (gradient of objective function with respect to the surface movement) with the parametric design velocities (movement of the surface due to a CAD parameter perturbation) in order to compute the gradient of the objective function with respect to CAD variables.
For a successful implementation of shape optimization strategies in practical industrial cases, the choice of design variables or parameterisation scheme used for the model to be optimized plays a vital role. Where the goal is to base the optimization on a CAD model the choices are to use a NURBS geometry generated from CAD modelling software, where the position of the NURBS control points are the optimisation variables [2] or to use the feature based CAD model with all of the construction history to preserve the design intent [3]. The main advantage of using the feature based model is that the optimized model produced can be directly used for the downstream applications including manufacturing and process planning.
This paper presents an approach for optimization based on the feature based CAD model, which uses CAD parameters defining the features in the model geometry as the design variables. In order to capture the CAD surface movement with respect to the change in design variable, the “Parametric Design Velocity” is calculated, which is defined as the movement of the CAD model boundary in the normal direction due to a change in the parameter value.
The approach presented here for calculating the design velocities represents an advancement in terms of capability and robustness of that described by Robinson et al. [3]. The process can be easily integrated to most industrial optimisation workflows and is immune to the topology and labelling issues highlighted by other CAD based optimisation processes. It considers every continuous (“real value”) parameter type as an optimisation variable, and it can be adapted to work with any CAD modelling software, as long as it has an API which provides access to the values of the parameters which control the model shape and allows the model geometry to be exported. To calculate the movement of the boundary the methodology employs finite differences on the shape of the 3D CAD models before and after the parameter perturbation. The implementation procedure includes calculating the geometrical movement along a normal direction between two discrete representations of the original and perturbed geometry respectively. Parametric design velocities can then be directly linked with adjoint surface sensitivities to extract the gradients to use in a gradient-based optimization algorithm.
The optimisation of a flow optimisation problem is presented, in which the power dissipation of the flow in an automotive air duct is to be reduced by changing the parameters of the CAD geometry created in CATIA V5. The flow sensitivities are computed with the continuous adjoint method for a laminar and turbulent flow [4] and are combined with the parametric design velocities to compute the cost function gradients. A line-search algorithm is then used to update the design variables and proceed further with optimisation process.

Optimisation of radiotherapy for carcinoma of the parotid gland: a comparison of conventional, three-dimensional conformal, and intensity-modulated techniques

Background and purpose: To compare external beam radiotherapy techniques for parotid gland tumours using conventional radiotherapy (RT), three-dimensional conformal radiotherapy (3DCRT), and intensity-modulated radiotherapy (IMRT). To optimise the IMRT techniques, and to produce an IMRT class solution.Materials and methods: The planning target volume (PTV), contra-lateral parotid gland, oral cavity, brain-stem, brain and cochlea were outlined on CT planning scans of six patients with parotid gland tumours. Optimised conventional RT and 3DCRT plans were created and compared with inverse-planned IMRT dose distributions using dose-volume histograms. The aim was to reduce the radiation dose to organs at risk and improve the PTV dose distribution. A beam-direction optimisation algorithm was used to improve the dose distribution of the IMRT plans, and a class solution for parotid gland IMRT was investigated.Results: 3DCRT plans produced an equivalent PTV irradiation and reduced the dose to the cochlea, oral cavity, brain, and other normal tissues compared with conventional RT. IMRT further reduced the radiation dose to the cochlea and oral cavity compared with 3DCRT. For nine- and seven-field IMRT techniques, there was an increase in low-dose radiation to non-target tissue and the contra-lateral parotid gland. IMRT plans produced using three to five optimised intensity-modulated beam directions maintained the advantages of the more complex IMRT plans, and reduced the contra-lateral parotid gland dose to acceptable levels. Three- and four-field non-coplanar beam arrangements increased the volume of brain irradiated, and increased PTV dose inhomogeneity. A four-field class solution consisting of paired ipsilateral coplanar anterior and posterior oblique beams (15, 45, 145 and 170o from the anterior plane) was developed which maintained the benefits without the complexity of individual patient optimisation.Conclusions: For patients with parotid gland tumours, reduction in the radiation dose to critical normal tissues was demonstrated with 3DCRT compared with conventional RT. IMRT produced a further reduction in the dose to the cochlea and oral cavity. With nine and seven fields, the dose to the contra-lateral parotid gland was increased, but this was avoided by optimisation of the beam directions. The benefits of IMRT were maintained with three or four fields when the beam angles were optimised, but were also achieved using a four-field class solution. Clinical trials are required to confirm the clinical benefits of these improved dose distributions.

Design, optimisation and characterisation of polymeric microneedle arrays prepared by a novel laser-based micromoulding technique.

PURPOSE:
Design and evaluation of a novel laser-based method for micromoulding of microneedle arrays from polymeric materials under ambient conditions. The aim of this study was to optimise polymeric composition and assess the performance of microneedle devices that possess different geometries.
METHODS:
A range of microneedle geometries was engineered into silicone micromoulds, and their physicochemical features were subsequently characterised.
RESULTS:
Microneedles micromoulded from 20% w/w aqueous blends of the mucoadhesive copolymer Gantrez® AN-139 were surprisingly found to possess superior physical strength than those produced from commonly used pharma polymers. Gantrez® AN-139 microneedles, 600 µm and 900 µm in height, penetrated neonatal porcine skin with low application forces (>0.03 N per microneedle). When theophylline was loaded into 600 µm microneedles, 83% of the incorporated drug was delivered across neonatal porcine skin over 24 h. Optical coherence tomography (OCT) showed that drug-free 600 µm Gantrez® AN-139 microneedles punctured the stratum corneum barrier of human skin in vivo and extended approximately 460 µm into the skin. However, the entirety of the microneedle lengths was not inserted.
CONCLUSION:
In this study, we have shown that a novel laser engineering method can be used in micromoulding of polymeric microneedle arrays. We are currently carrying out an extensive OCT-informed study investigating the influence of microneedle array geometry on skin penetration depth, with a view to enhanced transdermal drug delivery from optimised laser-engineered Gantrez® AN-139 microneedles.

Aerofoil Optimisation Using CST Parameterisation in SU2

This paper describes an implementation of the popular method of Class-Shape Transformation for aerofoil design within SU2 software framework. To exploit the adjoint based methods for aerodynamic optimisation within the SU2, a formulation to obtain geometric sensitivities from the new parameterisation is introduced, enabling the calculation of gradients with respect to new design variables. To assess the accuracy and efficiency of the alternative approach, two transonic optimisation problems are investigated: an inviscid problem with multiple constraints and a viscous problems without any constraints. Results show the new parameterisation obtaining reliable optimums, with similar levels of
performance of the software native parameterisations.

Aerodynamic optimization using Adjoint methods and parametric CAD models

This paper describes an implementation of a method capable of integrating parametric, feature based, CAD models based on commercial software (CATIA) with the SU2 software framework. To exploit the adjoint based methods for aerodynamic optimisation within the SU2, a formulation to obtain geometric sensitivities directly from the commercial CAD parameterisation is introduced, enabling the calculation of gradients with respect to CAD based design variables. To assess the accuracy and efficiency of the alternative approach, two aerodynamic optimisation problems are investigated: an inviscid, 3D, problem with multiple constraints, and a 2D high-lift aerofoil, viscous problem without any constraints. Initial results show the new parameterisation obtaining reliable optimums, with similar levels of performance of the software native parameterisations. In the final paper, details of computing CAD sensitivities will be provided, including accuracy as well as linking geometric sensitivities to aerodynamic objective functions and constraints; the impact in the robustness of the overall method will be assessed and alternative parameterisations will be included.

Optimisation of a droplet digital PCR assay for the diagnosis of Schistosoma japonicum infection: A duplex approach with DNA binding dye chemistry

Schistosomiasis is a chronically debilitating helminth infection with a significant socio-economic and public health impact. Accurate diagnostics play a pivotal role in achieving current schistosomiasis control and elimination goals. However, many of the current diagnostic procedures, which rely on detection of schistosome eggs, have major limitations including lack of accuracy and the inability to detect pre-patent infections. DNA-based detection methods provide a viable alternative to the current tests commonly used for schistosomiasis diagnosis. Here we describe the optimisation of a novel droplet digital PCR (ddPCR) duplex assay for the diagnosis of Schistosoma japonicum infection which provides improved detection sensitivity and specificity. The assay involves the amplification of two specific and abundant target gene sequences in S. japonicum; a retrotransposon (SjR2) and a portion of a mitochondrial gene (nad1). The assay detected target sequences in different sources of schistosome DNA isolated from adult worms, schistosomules and eggs, and exhibits a high level of specificity, thereby representing an ideal tool for the detection of low levels of parasite DNA in different clinical samples including parasite cell free DNA in the host circulation and other bodily fluids. Moreover, being quantitative, the assay can be used to determine parasite infection intensity and, could provide an important tool for the detection of low intensity infections in low prevalence schistosomiasis-endemic areas.