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Congestion issues are becoming more prevalent as the share of variable renewable energy increases. Conventional methods to mitigate them, such as building new lines, require significant lead time and upfront investment costs. Thus, alternative approaches that deal with congestion problems faster and in a more efficient manner have gained significant interest. In this paper, a modular mobile power flow controller (MPFC) is proposed that can redirect power flow from congested lines to adjacent ones by modifying the line reactance, but also offers additional significant advantages: a) scalability due to the modular nature that allows the MPFC to be sized optimally for addressing specific system needs, b) redeployability due to the mobile deployment method that allows the MPFC to be rapidly deployed and easily removed and redeployed to another location, and c) replicability stemming from the minimal construction requirements for connecting the MPFC to the transmission grid. The whole process of planning, installing, operating and redeploying the MPFC on a 150kV transmission system is thoroughly documented and studied. Overall, this paper presents a novel technology that can offer a quicker and at a lower cost solution to congestion issues, while reducing the impact of new infrastructure on communities and the environment.

The continuous penetration of renewable energy resources (RES) into the energy mix and the transition of the traditional electric grid towards a more intelligent, flexible and interactive system, has brought electrical load forecasting to the foreground of smart grid planning and operation. Predicting the electric load is a challenging task due to its high volatility and uncertainty, either when it refers to the distribution system or to a single household. In this paper, a novel methodology is introduced which leverages the advantages of the state-of-the-art deep learning algorithms and specifically the Convolution Neural Nets (CNN). The main feature of the proposed methodology is the exploitation of the statistical properties of each time series dataset, so as to optimize the hyper-parameters of the neural network and in addition transform the given dataset into a form that allows maximum exploitation of the CNN algorithm’s advantages. The proposed algorithm is compared with the LSTM (Long Short Term Memory) technique which is the state of the art solution for electric load forecasting. The evaluation of the algorithms was conducted by employing three open-source, publicly available datasets. The experimental results show strong evidence of the effectiveness of the proposed methodology.

The European Commission’s Target Model’s main objective is to integrate European electricity markets, leading to a single internal energy market and guaranteeing the instantaneous balance between electricity generation and demand. According to the target model for electricity trading, proposed by the European Network Transmission System Operators for Electricity (ENTSO-E), within each zone, electricity can be traded freely without taking into consideration network limitations. In contrast, for cross-border trading, the exchanges with other market areas are taken into account. Cross-border trade poses a further burden on the interconnection lines, resulting in increasing network congestion, which in turn restricts electricity trading. Thus, calculating the available capacity for trade has a significant ramification on the market. Today, the Available Transfer Capacity (ATC) mechanism dominates cross-border trading, but this methodology may be replaced by the Flow-Based (FB) approach across Europe. This paper investigates both approaches regarding the cross-border congestion management under the market coupling procedure. In our case study, the Southeast Europe (SEE) region is taken into consideration; it consists of both the FB and ATC approach in a five country (Greece, North Macedonia, Bulgaria, Serbia, and Romania) scenario. The purpose of our tests is to perform, compare, and evaluate the effectiveness of each method for the SEE region, while the main findings are the maximization of social welfare, better cross-border trading opportunities, and price convergence via the FB method.

Italy, Greece, and, to a lesser degree, Bulgaria have experienced fast growth in their renewable generation capacity (RESc) over the last several years. The consequences of this fact include a decrease in spot wholesale prices in electricity markets and a significant effect on cross border trading (CBT) among neighboring interconnected countries. In this work, we empirically analyzed historical data on fundamental market variables (i.e., spot prices, load, RES generation) as well as CBT data (imports, exports, commercial schedules, net transfer capacities, etc.) on the Greek, Italian, and Bulgarian electricity markets by applying the Granger causality connectivity analysis (GCCA) approach. The aim of this analysis was to detect all possible interactions among the abovementioned variables, focusing in particular on the effects of growing shares of RES generation on the commercial electricity trading among the abovementioned countries for the period 2015−2018. The key findings of this paper are summarized as the following: The RES generation in Italy, for the period examined, drives the spot prices in Greece via commercial schedules. In addition, on average, spot price fluctuations do not affect the commercial schedules of energy trading between Greece and Bulgaria.

We study the co-evolution of the dynamics or co-movement of two electricity markets, the Italian and Greek, by studying the dynamics of their wholesale day-ahead prices, simultaneously in the time-frequency domain. Co-movement is alternatively referred as market integration in financial economics and markets are internationally integrated if the reward for risk is identical regardless the market one trades in. The innovation of this work is the application of wavelet analysis and more specifically the wavelet coherence to estimate the dynamic interaction between these two prices. Our method is compared to other generic econometric tools used in Economics and Finance namely the dynamic correlation and coherence analysis, to study the co-movement of variables of the type related to these two fields. Our study reveals valuable information that we believe will be extremely useful to the authorities as well as other agents participating in these markets to better prepare the national markets towards the European target model, a framework in which the two markets will be coupled.

In this paper we examine and compare the efficiency of four European electricity markets (NordPool, Italian, Spanish and Greek) of different microstructure and level of maturity, by testing the weak form of the Efficient Market Hypothesis (EMH). To quantify the level of efficiency deviation of each market from the ‘ideal’ or ‘benchmark market of random walk’, we have constructed a Composite Electricity Market Efficiency Index (EMEI), inspired by similar works on other energy commodities. The proposed index consists of linear and nonlinear components each one measuring a different feature or dimension of the market efficiency such as its complexity, fractality, entropy, long-term memory or correlation, all connected to the associated benchmark values of the Random Walk Process (RWP). The key findings are that overall, all examined electricity markets are inefficient in respect to the weak form of EMH and the less inefficient market, as measured by the EMEI is the NordPool, closely followed by the Spanish market, with the Italian being the third. The most inefficient market is the Greek one. These results are in accordance with the predominant view about the maturity of these markets. This study contributes significantly on improving the research framework in developing consistent and robust tools for efficiency measurement, while the proposed index can be a valuable tool in designing improved guidelines towards enhancing the efficiency of electricity markets.

In this work we propose a new hybrid model, a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004–2014. PC-regression is shown to effectively capture the intraday, intraweek and annual patterns of load. We compare our model with a number of classical statistical approaches (Holt-Winters exponential smoothing of its generalizations Error-Trend-Seasonal, ETS models, the Seasonal Autoregressive Moving Average with exogenous variables, Seasonal Autoregressive Integrated Moving Average with eXogenous (SARIMAX) model as well as with the more sophisticated artificial intelligence models, Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Using a number of criteria for measuring the quality of the generated in-and out-of-sample forecasts, we have concluded that the forecasts of our hybrid model outperforms the ones generated by the other model, with the SARMAX model being the next best performing approach, giving comparable results. Our approach contributes to studies aimed at providing more accurate and reliable load forecasting, prerequisites for an efficient management of modern power systems.

A significant number of research papers regarding biosensor-related assays for key food safety pathogens based on the use of mammalian cells has been reported. In this study, the Salmonella typhimurium infection progression was monitored in the human colon cancer cell line Caco-2 and the mucus-secreting HT29-MTX-E12, after treatment with five different bacterial MOI for 30 min by comparing the alterations of frequencies recordings with impedance spectroscopy measurements. For this purpose, bacterial adhesion and invasion assays were initially performed. Then, the data obtained from impedance spectroscopy recordings were compared to cell viability data derived from the MTT uptake cell proliferation assay as well as from live cell analysis assays of mitochondrial membrane potential alterations. From our findings a concentration-dependent increase in bacterial colonies occurring from invaded cells was observed upon a higher multiplicity of infection (MOI) bacterial infection at both cell lines. On the contrary, the bacteria infection did not have any impact on the viability of the cells after 1 h of treatment. Differential results were obtained from the measurement of mitochondrial potential at both cell lines. Finally, the impedance values recorded from the 2D, and 3D cultures were concentration-dependent for both cell lines whereas a characteristic pattern specific to each cell line was revealed. Our results indicate that human cell-based bio-electric assays can be a valuable tool for obtaining a unique fingerprint for each bacterial infection in the near future.

Polyploidy contributes to extensive intratumor genomic heterogeneity that characterizes advanced malignancies and is thought to limit the efficiency of current cancer therapies. It has been shown that telomere deprotection in p53-deficient mouse embryonic fibroblasts leads to high rates of polyploidization. We now show that tumor genome evolution through whole-genome duplication occurs in ∼15% of the karyotyped human neoplasms and correlates with disease progression. In a panel of human cancer and transformed cell lines representing the two known types of genomic instability (chromosomal and microsatellite), as well as the two known pathways of telomere maintenance in cancer (telomerase activity and alternative lengthening of telomeres), telomere dysfunction-driven polyploidization occurred independently of the mutational status of p53. Depending on the preexisting context of telomere maintenance, telomerase activity and its major components, human telomerase reverse transcriptase (hTERT) and human telomerase RNA component (hTERC), exert both reverse transcriptase-related (canonical) and noncanonical functions to affect tumor genome evolution through suppression or induction of polyploidization. These new findings provide a more complete mechanistic understanding of cancer progression that may, in the future, lead to novel therapeutic interventions.

In an attempt to develop new potent anti-inflammatory agents, a cinnamic -amino acid hybrid molecule was synthesized and in silico drug-likeness, in vitro COX-2 inhibition, and pharmacokinetic properties were studied. The results showed high cyclooxygenase inhibitory activity (IC50 = 6 µM) and favorable pharmacokinetic properties, being orally bioavailable according to Lipinski’s rule of five, making this compound a possible lead to design and develop potent COX inhibitors. The new compound, in comparison with its cinnamic acid precursor (E)-(3-(4-((4-bromobenzyl)oxy)phenyl)acrylic acid, showed improved biological activities. Compound ethyl (E)-(3-(4-((4-bromobenzyl)oxy)phenyl)acryloyl)glycinate can be used as a lead for the synthesis of more effective hybrids.

As a consequence of the progress of the global vaccination against the COVID-19 disease, fast, accurate and affordable assays are needed for monitoring the efficiency of developing immunity against the coronavirus at the population level. In this context, we herewith report the proof-of-concept development of an innovative bioelectric biosensor for the ultra-detection (in less than three minutes) of IgG antibodies against the SARS-CoV-2 S1 spike antigen. The biosensor comprises a disposable set of screen-printed electrodes upon which are immobilized cells engineered to bear the S1 protein on their surface. When anti-S1 antibodies are presented to the engineered cell population, a rapid, specific, and selective change of the cell membrane potential occurs; this is in turn recorded by a bespoke portable potentiometer. End results are communicated via Bluetooth to a smartphone equipped with a customized user interface. By using the novel biosensor, anti-S1 antibodies could be detected at concentrations as low as 5 ng/mL. In a preliminary clinical trial, positive results were derived from patients vaccinated or previously infected by the virus. Selectivity over other respiratory viruses was demonstrated by the lack of cross-reactivity to antibodies against rhinovirus. After further clinical validation and extension to also screen IgM, IgA and possible neutralizing antibodies, our approach is intended to facilitate the mass and reliable detection of antibodies in the early stages following vaccination and to monitor the duration and level of acquired immunity both in a clinical and self-testing environment.