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The Bolshevik Revolution in Russia (1917), the dissolution of the Tsarist Army and the collapse of the Eastern Front also had an impact on the security in Bessarabia. For this reason, the political authorities in Chisinau requested the Romanian Government to authorize the intervention of the Royal Army in the east of the Prut. During the military operations, the Romanian Army was accompanied by a number of approximately 40 mobilized priests. By fulfilling religious requirements, raising morale, maintaining discipline and promoting brotherly behavior towards the locals, they offered real spiritual guidance to the troops. In the interaction with the native civilian population, the military clergy officiated Orthodox acts of worship, they delivered patriotic and moralizing speeches and explained the national history, carrying out a true apostolic mission. They found on spot that the belonging of the Bessarabians to the Romanian nation had been undermined for more than a century by the policy of denationalization, intensively patronized by the power of the Russian double-headed eagle, often with the help of the Church.

The holy act of reuniting with Bessarabia (March 27 / April 9, 1918) is intrinsically linked to the personality of the conservative politician Alexandru Marghiloman. According to him, the process of denationalization of Romanians had reached much more alarming levels in Bessarabia than in Transylvania. The brightest moment of his government was the union of Bessarabia with the Kingdom of Romania which interfered with both the military and the Orthodox church. The renowned author of the Political Notes 1897–1924 provides us with valuable information about the memorable historical moments and about the mood of the artisans of those long-awaited moments, integral parts of the multisecular project of National Integration. In the sense of the “Wallachian lord”, the union of Bessarabia with the Motherland, after 106 years of Tsarist occupation, was the fruit of divine providence.

The increasing importance of hydropower generation has led to the development of new smart technologies and the need for reliable and efficient equipment in this field. As long as hydropower plants are more complex to build up than other power plants, the operation regimes and maintenance activities become essential for the hydropower companies to optimize their performance, such that including the data-driven approaches in the decision-making process represents a challenge. In this paper, a comprehensive and multi-task framework integrated into a Knowledge Discovery module based on Data Mining to support the decisions of the operators from the control rooms and facilitate the transition from the classical to smart Supervisory Control and Data Acquisition (SCADA) system in hydropower plants has been designed, developed, and tested. It integrates tasks related to detecting the outliers through advanced statistical procedures, identifying the operating regimes through the patterns associated with typical operating profiles, and developing strategies for loading the generation units that consider the number of operating hours and minimize the water amount used to satisfy the power required by the system. The proposed framework has been tested using the SCADA system’s database of a hydropower plant belonging to the Romanian HydroPower Company. The framework can offer the operators from the control room comparative information for a time horizon longer than one year. The tests demonstrated the utility of a Knowledge Discovery module to ensure the transition toward smart SCADA systems that will help the decision-makers improve the management of the hydropower plants.

In the original publication [...]

In the original publication [...]

Currently, all major vehicle manufacturers agree that the future of mobility is electric. All of them began selling electric vehicles (EVs). These types of vehicles require charging, which in turn will have an impact on power distribution systems. All the studies show that the impact on power systems will be significant and may be disastrous if there will be no investments. Unmanaged EV charging can lead to a significant increase in energy costs due to increased level of investment in energy infrastructure. This paper proposes a decentralized EV charging control (DEV-CC) system that can be executed by the existing on-board electronic control units (ECUs) and uses dedicated short-range communication (DSRC) to establish communication between EVs. The proposed DEV-CC adapts the EV charging power depending on the low-voltage distribution network (LVDN) voltage levels measured by the EVs themselves. The main purpose of the proposed DEV-CC is to charge all the EVs connected to the LVDN without allowing the voltage to drop below the imposed limit. As the results show, the proposed DEV-CC manages to charge all EVs while maintaining the voltage levels within the LVDN above the allowable limits. The proposed DEV-CC does not require any investments from the distribution system operator (DSO), can be implemented on EVs with minimal costs and is a viable solution to expensive smart grid systems.

In this paper, we used the single-photon emission computerized tomography (SPECT) imaging technique to visualize the deficiency of dopamine-generated patterns inside the brain. These patterns are used to establish a patient’s disease progression, which helps distinguish the patients into different categories. Furthermore, we used a convolutional neural network (CNN) model to classify the patients based on the dopamine level inside the brain. The dataset used throughout this paper is the Parkinson’s progressive markers initiative (PPMI) dataset. The collected dataset was pre-processed and data amplification was performed to balance the imbalanced dataset. A CNN-based neural network was defined to classify input SPECT images into four categories. The motivation behind the proposed model is to reduce the number of resources consumed while maintaining the performance of the classification model. This will help the healthcare ecosystem run the classification model on mobile devices. The proposed model contains 14 layers with input layers, convolutional layers, max-pool layers, flatten layers, and dense layers with different dimensions. The dense layer classifies the patients into four different categories, including PSD, healthy control, scans without evidence of dopaminergic deficit (SWEDD), and GenReg PSD from the entire SPECT imaging dataset, which is used to establish the disease progression of different patients using SPECT images. The proposed model is trained with a large dataset with 58,692 images for training and 11,738 images for validation, and 7826 for testing. The proposed model outperforms the classification models from the surveyed papers. The proposed model’s accuracy is 0.889, recall is 0.9012, the precision is 0.9104, and the F1-score is 0.9057.

Throughout the history of modern finance, very few financial instruments have been as strikingly volatile as cryptocurrencies. The long-term prospects of cryptocurrencies remain uncertain; however, taking advantage of recent advances in neural networks and volatility, we show that the trading algorithms reinforced by short-term price predictions are bankable. Traditional trading algorithms and indicators are often based on mean reversal strategies that do not advantage price predictions. Furthermore, deterministic models cannot capture market volatility even after incorporating price predictions. Thus motivated by these issues, we integrate randomness in the price prediction models to simulate stochastic behavior. This paper proposes hybrid trading strategies that take advantage of the traditional mean reversal strategies alongside robust price predictions from stochastic neural networks. We trained stochastic neural networks to predict prices based on market data and social sentiment. The backtesting was conducted on three cryptocurrencies: Bitcoin, Ethereum, and Litecoin, for over 600 days from August 2017 to December 2019. We show that the proposed trading algorithms are better when compared to the traditional buy and hold strategy in terms of both stability and returns.

The real-time control of optimal power flow (OPF) in electric networks represents, in the last period, a challenge for the Distribution Network Operators (DNOs) and Transmission System Operators (TSOs) under the conditions of large-scale integration of renewable energy sources. The paper focused on the voltage management in the electric networks with wind farms connected, proposing a fast Successive Quadratic Programming (SQP)-based centralized control (CC) methodology of the on-load tap changers (OLTC) corresponding to the transformers from the electric substations, which can be included in the Optimal Power Flow (OPF) module of the Supervisory Control and Data Acquisition (SCADA) Master System. The SQP-CC aims to ensure an optimal voltage level inside a variation range, as narrow as possible, which leads to minimize power losses in the electric networks. Treating the OPF problem as an SQP problem and accelerating convergence using the conjugate reduced gradient method led to very few iterations and a fast computational time, recommending the implementation of the methodology for real-time work. The effectiveness of the SQP-CC was demonstrated in a test network having two voltage levels (220 and 110 kV) with two wind farms integrated, considering two scenarios, without and with wind farms connected. The optimal tap positions of the transformers led to very close voltage variations between the buses of the network, quantified through an average voltage drop between the initial and final buses by 0.004 pu compared with 0.031 pu recorded in the scenario with both wind farms injecting power into the network and without coordinated control of the OLTCs. The energy-saving was over 30% in both scenarios, 33.5% (without wind farms) and 41.27% (both wind farms injecting power in the network). These results highlighted the positive effects of the proposed SQP-CC methodology on the real-time optimal operation of the electric networks integrating wind farms.

In the paper, a coordinated control methodology of single-phase (1-P) end-users switching operations on the phases of an active electric distribution network (AEDN) has been proposed to obtain a minimum unbalance degree at the coupling common point (CCP) level with the main distribution system. The phase load balancing (PLB) process considers the smart devices that switch the 1-P end-users (consumers and prosumers) from one phase to another to compensate for the phase load unbalance. The proposed methodology has been tested successfully in an AEDN belonging to a Romanian Distribution Network Operator (DNO) containing 114 end-users (104 consumers/10 prosumers) integrated into the Smart Metering System (SMS). The optimal solution leads to a value of the objective function by 1.00, represented by the unbalance factor (UF), which could be identified with the ideal target. A comparative analysis was conducted considering other possible PLB cases (the consumer-level PLB and prosumer-level PLB), obtaining similar values of the UF (1.027 vs. 1.028), slightly higher than in the hybrid-level PLB. Additionally, the significant technical benefits were quantified through an energy-saving of 58.73% and decreasing the phase voltage unbalance rate by 91% compared to the initial case (without PLB). These results emphasized the positive impact of the proposed coordinated control methodology on the PLB process and evidenced its effectiveness and applicability in the AEDNs.

Demand Side Management (DSM) is becoming necessary in residential electricity distribution networks where local electricity trading is implemented. Amongst the DSM tools, Demand Response (DR) is used to engage the consumers in the market by voluntary disconnection of high consumption receptors at peak demand hours. As a part of the transition to Smart Grids, there is a high interest in DR applications for residential consumers connected in intelligent grids which allow remote controlling of receptors by electricity distribution system operators and Home Energy Management Systems (HEMS) at consumer homes. This paper proposes a novel algorithm for multi-objective DR optimization in low voltage distribution networks with unbalanced loads, that takes into account individual consumer comfort settings and several technical objectives for the network operator. Phase load balancing, two approaches for minimum comfort disturbance of consumers and two alternatives for network loss reduction are proposed as objectives for DR. An original and faster method of replacing load flow calculations in the evaluation of the feasible solutions is proposed. A case study demonstrates the capabilities of the algorithm.

In the electric distribution systems, the “Smart Grid” concept is implemented to encourage energy savings and integration of the innovative technologies, helping the distribution network operators (DNOs) in choosing the investment plans which lead to the optimal operation of the networks and increasing the energy efficiency. In this context, a new phase load balancing algorithm was proposed to be implemented in the low voltage distribution networks with hybrid structures of the consumption points (switchable and non-switchable consumers). It can work in both operation modes (real-time and off-line), uploading information from different databases of the DNO which contain: The consumers’ characteristics, the real loads of the consumers integrated into the smart metering system (SMS), and the typical load profiles for the consumers non-integrated in the SMS. The algorithm was tested in a real network, having a hybrid structure of the consumption points, on a by 24-h interval. The obtained results were analyzed and compared with other algorithms from the heuristic (minimum count of loads adjustment algorithm) and the metaheuristic (particle swarm optimization and genetic algorithms) categories. The best performances were provided by the proposed algorithm, such that the unbalance coefficient had the smallest value (1.0017). The phase load balancing led to the following technical effects: decrease of the average current in the neutral conductor and the energy losses with 94%, respectively 61.75%, and increase of the minimum value of the phase voltage at the farthest pillar with 7.14%, compared to the unbalanced case.

A growing number of households benefit from government subsidies to install renewable generation facilities such as PV panels, used to gain independence from the grid and provide cheap energy. In the Romanian electricity market, these prosumers can sell their generation surplus only at regulated prices, back to the grid. A way to increase the number of prosumers is to allow them to make higher profit by selling this surplus back into the local network. This would also be an advantage for the consumers, who could pay less for electricity exempt from network tariffs and benefit from lower prices resulting from the competition between prosumers. One way of enabling this type of trade is to use peer-to-peer contracts traded in local markets, run at microgrid (μG) level. This paper presents a new trading platform based on smart peer-to-peer (P2P) contracts for prosumers energy surplus trading in a real local microgrid. Several trading scenarios are proposed, which give the possibility to perform trading based on participants’ locations, instantaneous active power demand, maximum daily energy demand, and the principle of first come first served implemented in an anonymous blockchain trading ledger. The developed scheme is tested on a low-voltage (LV) microgrid model to check its feasibility of deployment in a real network. A comparative analysis between the proposed scenarios, regarding traded quatities and financial benefits is performed.