Near real-time analysis of active distribution networks in a Digital Twin framework. A real case study

The growth of distributed generation and the need of increasing Distribution Network (DN) resilience is encouraging Distribution System Operators (DSO) to increase awareness about the real-time status of the network as well as to actively manage flexible energy resources for improving system performances. In this context, Digital Twin (DT) is an enabling technology for a low-cost distributed framework that supports DN management. DT in the power system can be exploited taking advantage of the successful experiences in other sectors (e.g., smart manufacturing and building automation). This article presents a real case study of a DT development and its integration with an existing DN. The DT system architecture is based on the recent standards whilst main DT components have been originally developed, enabling near real-time services such as data collection, state estimation, and flexibility calculator. The individual performances of the integrated tools and the reliability of DT were tested and validated during one month of continuous operation. During the operation, good service continuity and accuracy performances were reported. Results from the flexibility calculator show the effectiveness of the proposed strategies that can improve the energy efficiency of the DN by increasing local self-consumption of Renewable Energy Sources (RES) production

A Low-Cost Smart Monitoring Device for Demand-Side Response Campaigns

The energy transition requires an increasing penetration of renewable resources, particularly at MV/LV levels. The emerging production scheme is characterized by distributed power plants, imposes a capillary control of production and consumption among the distribution network (DN). The implementation of demand-side response (DSR) campaigns is widely seen as a solution that can increase grid stability, but they require a complex and expensive monitoring infrastructure to select the optimal operating point of the production/consumption systems. This paper suggests a cheap and reliable smart monitoring device based on Raspberry Pi technology. The communication infrastructure adopted in the smart building of ASM S.p.A., the distribution system operator (DSO) of Terni city, shows the feasibility of implementing this prototype on a large scale

A Low-Cost Smart Monitoring Device for Demand-Side Response Campaigns

The energy transition requires an increasing penetration of renewable resources, particularly at MV/LV levels. The emerging production scheme is characterized by distributed power plants, imposes a capillary control of production and consumption among the distribution network (DN). The implementation of demandside response (DSR) campaigns is widely seen as a solution that can increase grid stability, but they require a complex and expensive monitoring infrastructure to select the optimal operating point of the production/consumption systems. This paper suggests a cheap and reliable smart monitoring device based on Raspberry Pi technology. The communication infrastructure adopted in the smart building of ASM S.p.A., the distribution system operator (DSO) of Terni city, shows the feasibility of implementing this prototype on a large scale

Automated algorithm selection: survey and perspectives

It has long been observed that for practically any computational problem that has been intensely studied, different instances are best solved using different algorithms. This is particularly pronounced for computationally hard problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each problem instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-instance algorithm selection problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial problems, including propositional satisfiability and AI planning. Per-instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable problem instance features provide the basis for effective per-instance algorithm selection systems, we also provide an overview of such features for discrete and continuous problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.Pascal Kerschke, Holger H. Hoos, Frank Neumann, Heike Trautman

‘MCC’ protein interacts with E-cadherin and β-catenin strengthening cell–cell adhesion of HCT116 colon cancer cells

E-cadherin and β-catenin are key proteins that are essential in the formation of the epithelial cell layer in the colon but their regulatory pathways that are disrupted in cancer metastasis are not completely understood. Mutated in colorectal cancer (MCC) is a tumour suppressor gene that is silenced by promoter methylation in colorectal cancer and particularly in patients with increased lymph node metastasis. Here, we show that MCC methylation is found in 45% of colon and 24% of rectal cancers and is associated with proximal colon, poorly differentiated, circumferential and mucinous tumours as well as increasing T stage and larger tumour size. Knockdown of MCC in HCT116 colon cancer cells caused a reduction in E-cadherin protein level, which is a hallmark of epithelial–mesenchymal transition in cancer, and consequently diminished the E-cadherin/β-catenin complex. MCC knockdown disrupted cell–cell adhesive strength and integrity in the dispase and transepithelial electrical resistance assays, enhanced hepatocyte growth factor-induced cell scatter and increased tumour cell invasiveness in an organotypic assay. The Src/Abl inhibitor dasatinib, a candidate anti-invasive drug, abrogated the invasive properties induced by MCC deficiency. Mechanistically, we establish that MCC interacts with the E-cadherin/β-catenin complex. These data provide a significant advance in the current understanding of cell–cell adhesion in colon cancer cells