Application of large underground seasonal thermal energy storage in district heating system: A model-based energy performance assessment of a pilot system in Chifeng, China

Seasonal thermal energy storage (STES) technology is a proven solution to resolve the seasonal discrepancy between heating energy generation from renewables and building heating demands. This research focuses on the performance assessment of district heating (DH) systems powered by low-grade energy sources with large-scale, high temperature underground STES technology. A pilot DH system, located in Chifeng, China that integrates a 0.5 million m3 borehole thermal energy storage system, an on-site solar thermal plant and excess heat from a copper plant is presented. The research in this paper adopts a model-based approach using Modelica to analyze the energy performance of the STES for two district heating system configurations. Several performance indicators such as the extraction heat, the injection heat and the storage coefficient are selected to assess the STES system performance. Results show that a lower STES discharge temperature leads to a better energy performance. A sensitivity analysis of the site properties illustrates that the thermal conductivity of soil is the most influential parameter on the STES system performance. The long-term performance of the STES is also discussed and a shorter stabilization time between one and two years could be achieved by discharging the STES at a lower temperature.This research is part of the seasonal storage for solar and industrial waste heat utilization for urban district heating project funded by the Joint Scientific Thematic Research Programme (JSTP)–Smart Energy in Smart Cities. We gratefully acknowledge the financial support from the Netherlands Organisation for Scientific Research (NWO). We would also like to thank our research partners from Tsinghua University working on the project of the International S&T Cooperation Programof China (ISTCP) (project No. 2015DFG62410). Without their efforts, we would not have been able to obtain the technical data to conduct the case study

Combining point cloud and surface methods for modeling partial shading impacts of trees on urban solar irradiance

Although trees and urban vegetation have a significant influence on solar irradiation in the built environment, their impact on daylight and energy consumption is often not considered in building performance and urban environment simulation studies. This paper presents a novel method for comprehensive solar irradiance assessment that considers the dynamic partial shading impacts from trees. The proposed method takes urban point clouds as input and consists of three subsequent steps: (a) DGCNN-based segmentation, (b) fusion model generation, (c) matrix-based irradiance calculation. The method is validated by comparing model outputs with field measurement data, and an inter-model comparison with eleven state-of-the-art tree shading modeling approaches. Analyses carried out on daily and long-term basis show that the proposed fusion model can significantly reduce simulation errors compared to alternative approaches, while limiting the required input data to a minimum. The primary source of uncertainty stems from mismatches between tree morphology in the fusion model and reality, attributable to phenological growth and seasonal variations.</p

Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p

Optimizing the total energy consumption and CO₂ emissions by distributing computational workload among worldwide dispersed data centers

Major internet service providers have built and are currently building the world's largest data centres (DCs), which has already resulted in significant global energy consumption. Energy saving measures, from chip to building level, have been introduced gradually in recent decades. However, there is further potential for savings by assessing the performance of different DCs on a wider scale and evaluating information technology (IT) workload distribution strategies among these DCs. This paper proposes a methodology to optimize the electricity consumption and CO2 emissions by distributing IT workload across multiple imaginary DCs. The DCs are modelled and controlled in a virtual test environment based on a building energy simulation (BES) tool (TRNSYS). A controller tool (Matlab) is used to support testing and tuning of the optimization algorithm. A case study, consisting of the distribution of IT workload across four different types of data centers in multiple locations with different climate conditions, is presented. The case study will illustrate.</p

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Holistické řízení datacenter: Metodika pro uvedení do provozu víceoborového řízení datacenter s použitím simulace budov

The world data center energy consumption has been growing rapidly and currently is estimated at 1,7–2,2% of the world-wide electricity consumption. Achieving sustainability in this sector calls for development new energy efficient strategies and measures. Current research deals with development of holistic operation i.e. operation, where all essential processes such as data processing, cooling and power delivery and supply (including renewable energy sources) are optimized a coordinated. Testing of modern operational strategies, which is necessary for development and commissioning, is not possible during the regular operation due to the risk of limitation of the services or outage of the data center operation. Any outage of the data center is related with financial and reputation losses. Therefore, the testing is extremely limited. Alternatively, building energy simulation may offer “safe” testing environment for advanced control algorithms and accelerate their implementation in practice. This paper describes a novel workflow for testing of modern control algorithm and new application of building energy simulation of data center