Enhanced frequency response from industrial heating loads for electric power systems

Increasing penetration of renewable generation results in lower inertia of electric power systems. To maintain the system frequency, system operators have been designing innovative frequency response products. Enhanced Frequency Response (EFR) newly introduced in the UK is an example with higher technical requirements and customized specifications for assets with energy storage capability. In this paper, a method was proposed to estimate the EFR capacity of a population of industrial heating loads, bitumen tanks, and a decentralized control scheme was devised to enable them to deliver EFR. Case study was conducted using real UK frequency data and practical tank parameters. Results showed that bitumen tanks delivered high-quality service when providing service-1-type EFR, but underperformed for service-2-type EFR with much narrower deadband. Bitumen tanks performed well in both high and low frequency scenarios, and had better performance with significantly larger numbers of tanks or in months with higher power system inertia

Feature Fusion and Detection in Alzheimer’s Disease Using a Novel Genetic Multi-Kernel SVM Based on MRI Imaging and Gene Data

© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Voxel-based morphometry provides an opportunity to study Alzheimer’s disease (AD) at a subtle level. Therefore, identifying the important brain voxels that can classify AD, early mild cognitive impairment (EMCI) and healthy control (HC) and studying the role of these voxels in AD will be crucial to improve our understanding of the neurobiological mechanism of AD. Combining magnetic resonance imaging (MRI) imaging and gene information, we proposed a novel feature construction method and a novel genetic multi-kernel support vector machine (SVM) method to mine important features for AD detection. Specifically, to amplify the differences among AD, EMCI and HC groups, we used the eigenvalues of the top 24 Single Nucleotide Polymorphisms (SNPs) in a p-value matrix of 24 genes associated with AD for feature construction. Furthermore, a genetic multi-kernel SVM was established with the resulting features. The genetic algorithm was used to detect the optimal weights of 3 kernels and the multi-kernel SVM was used after training to explore the significant features. By analyzing the significance of the features, we identified some brain regions affected by AD, such as the right superior frontal gyrus, right inferior temporal gyrus and right superior temporal gyrus. The findings proved the good performance and generalization of the proposed model. Particularly, significant susceptibility genes associated with AD were identified, such as CSMD1, RBFOX1, PTPRD, CDH13 and WWOX. Some significant pathways were further explored, such as the calcium signaling pathway (corrected p-value = 1.35 × 10−6) and cell adhesion molecules (corrected p-value = 5.44 × 10−4). The findings offer new candidate abnormal brain features and demonstrate the contribution of these features to AD.Peer reviewedFinal Published versio

A bidding system for peer-to-peer energy trading in a grid-connected microgrid

Peer-to-Peer (P2P) energy trading is a novel paradigm of power system operation, where people can generate their own energy from Renewable Energy Sources (RESs) in dwellings, offices and factories, and share it with each other locally. An architecture model was proposed to present the design and interoperability aspects of components for P2P energy trading in a microgrid. A specific Customer-to-Customer business model was introduced in a benchmark grid-connected microgrid based on the architecture model. The core component of a bidding system, called Elecbay, was also proposed and simulated using game theory. Test results show that P2P energy trading is able to balance local generation and demand, therefore, has a potential to enable a large penetration of RESs in the power grid

Thermal stability of ultrahard polycrystalline diamond composite materials

Thermal stability of the ultrahard polycrystalline diamond (UHPCD) composite material developed by the reinforcement of the polycrystalline diamond (PCD) with chemical vapor deposition (CVD) diamond has been investigated in a flow of argon at 1200 °C. The indentation, Raman spectra and wear test have been performed to compare hardness, C–C structure and wear resistance of untreated and thermal treated UHPCD. It has been shown that the hardness of CVD diamond in UHPCD attains 133±7 GPa after high pressure and high temperature, while after thermal treatment the hardness decreases to 109±3 GPa, and the wear resistance of the thermal treated UHPCD decreases from 0.17 to 0.6 mg/km. The narrowing of full width at half maximum and shift of Raman peak to lower frequencies of CVD diamond in thermal treated UHPCD imply a decrease of crystal structural defects and compressive stresses, which results in a drop of the hardness of CVD diamond in a thermal treated UHPCD. The higher wear rate of thermal treated UHPCD is due to the lower hardness.Досліджено термічну стабільність надтвердого полікристалічного алмазного (UHPCD) композиційного матеріалу, отриманого армуванням полікристалічного алмазу після хімічного осадження (CVD) алмазу в потоці аргону при 1200 °C. Для порівняння твердості, C–C-структури і зносостійкості необробленого та термообробленого UHPCD було досліджено заглиблення індентора, спектри комбінаційного розсіювання та знос. Показано, що твердість CVD-алмазу в UHPCD досягає 133±7 ГПа після дії високого тиску і високої температури, а після термообробки зменшується до 109±3 ГПа, зносостійкість UHPCD після термообробки зменшується від 0,17 до 0,6 мг/км. Звуження напівширини і зсув піку комбінаційного розсіювання в область низьких частот CVD-алмазу в термообробленому UHPCD характеризує зменшення кристалічних структурних дефектів і напружень стиску, що призводить до зниження твердості CVD-алмазу в термообробленому UHPCD. Вища швидкість зносу термообробленого UHPCD пов’язана з більш низькою твердістю.Исследована термическая стабильность сверхтвердого поликристаллического алмазного (UHPCD) композиционного материала, полученного армированием поликристаллического алмаза после химического осаждения (CVD) алмаза в потоке аргона при 1200 °C. Для сравнения твердости, C–C-структуры и износостойкости необработанного и термообработанного UHPCD были исследованы глубина проникновения индентора, спектры комбинационного рассеяния и износ. Показано, что твердость CVD-алмаза в UHPCD достигает 133±7 ГПа после действия высокого давления и высокой температуры, а после термической обработки уменьшается до 109±3 ГПа, износостойкость после термической обработки UHPCD уменьшается от 0,17 до 0,6 мг/км. Сужение полуширины и сдвиг пика комбинационного рассеяния в область низких частот CVD- алмаза в термообработанном UHPCD характеризует уменьшение кристаллических структурных дефектов и напряжений сжатия, что приводит к снижению твердости CVD-алмаза в термообработанном UHPCD. Более высокая скорость износа термически обработанного UHPCD связана с более низкой твердостью

Performance evaluation of peer-to-peer energy sharing models

With the increasing installation of distributed generation at the demand side, an increasing number of consumers become prosumers, and many peer-to-peer (P2P) energy sharing models have been proposed to reduce the energy bill of the prosumers through stimulating energy sharing and demand response. In this paper, a three-stage evaluation methodology is proposed to assess the economic performance of P2P energy sharing models. First of all, joint and individual optimization are established to identify the value contained in the energy sharing region. The overall energy bill of the prosumer population is then estimated through an agent-based modelling with reinforcement learning for each prosumer. Finally, a performance index is defined to quantify the economic performance of P2P energy sharing models. Simulation results verify the effectiveness of the proposed evaluation methodology, and compare three existing P2P energy sharing models in a variety of electricity pricing environments

A Triple-Network Dynamic Connection Study in Alzheimer's Disease

© 2022 Meng, Wu, Liang, Zhang, Xu, Yang and Meng. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/Alzheimer's disease (AD) was associated with abnormal organization and function of large-scale brain networks. We applied group independent component analysis (Group ICA) to construct the triple-network consisting of the saliency network (SN), the central executive network (CEN), and the default mode network (DMN) in 25 AD, 60 mild cognitive impairment (MCI) and 60 cognitively normal (CN) subjects. To explore the dynamic functional network connectivity (dFNC), we investigated dynamic time-varying triple-network interactions in subjects using Group ICA analysis based on k-means clustering (GDA-k-means). The mean of brain state-specific network interaction indices (meanNII) in the three groups (AD, MCI, CN) showed significant differences by ANOVA analysis. To verify the robustness of the findings, a support vector machine (SVM) was taken meanNII, gender and age as features to classify. This method obtained accuracy values of 95, 94, and 77% when classifying AD vs. CN, AD vs. MCI, and MCI vs. CN, respectively. In our work, the findings demonstrated that the dynamic characteristics of functional interactions of the triple-networks contributed to studying the underlying pathophysiology of AD. It provided strong evidence for dysregulation of brain dynamics of AD.Peer reviewedFinal Published versio

What do we visually focus on in a World Heritage Site? A case study in the Historic Centre of Prague

Since socio-economic development is associated with artificial construction, heritage environments must be protected and renewed while adapting to such development. Many World Heritage Sites’ visual integrity is endangered by new construction. The paper aims to explore people’s visual focus patterns concerning the integrity of heritage to ensure that traditional culture is not endangered by the construction and development of modern life, and to protect Outstanding Universal Values. In this study, visual heatmaps are generated to investigate people’s visual integrity in the Historic Centre of Prague from micro to macro viewpoints using an eye tracker. We found that humans’ perspectives are unobstructed or concentrated, and the view of main attractions is generally maintained by a buffer zone. However, newly constructed high-rise buildings can result in major visual concerns. Therefore, new buildings with large heights and strong contrasting colours should be restricted to World Heritage Sites. Moreover, complex artistic effects (facade midline, domes, mural painting, faces of sculptures) will likely attract people’s attention. In contrast, visual focus is not concentrated on greenery, roofs and floors. Accordingly, greenery could become a flexible space to serve as a background for buildings and landscape nodes. Furthermore, visual focal factors are associated with two significant aspects: people and the environment. Since people and transportation could pose visual concerns, tourism managers should optimise for characteristics such as controlling the density of pedestrian flow and planning parking spaces. The visual patterns identified could be useful for the design, conservation, and management of visual integrity in cultural heritage sites to avoid the spread of artificial constructions within the boundaries of heritage sites, which may lead to their being endangered or delisted