The inevitability and successive implementation of the elements of the European Union (EU) energy policy and the freedom of achieving the goals left in this regard for the member states should translate into actions taking the specificity of local markets into account, in order to carry out liberalization processes in a harmonious manner. In 2016, the European Commission published a package of guidance documents “Clean Energy for All Europeans” in the perspective of 2030, also known as the Winter Package. The recommendations contained in some of the documents assume the continuation of integration of markets in the national and regional dimension, setting ambitious targets in the field of decarbonization, the increase of energy efficiency and the increase of Renewable Energy Sources (RES) share in the energy balance of EU countries. The short time to carry out a thorough reconstruction of the energy-generating sector forces to seek solutions that are in line with the European Community recommendations and, at the same time, do not constitute an excessive burden for the national economy and legal order. One of the activities is to use the potential of micro-networks of local communities striving for energy independence based on their own energy sources and to create regulations enabling the neighborly exchange of energy. This mechanism works in the form of pilot projects in many locations around the world (Sonnen Group; Power Ledger). The paper presents the concept of functional and analytical assumptions for an exemplary structure of neighboring prosumers along with the presentation of simulation results based on real generation and consumption profiles and the presentation of investment profitability indicators for the proposed functional model.

Replacing mathematical models with artificial intelligence tools can play an important role in numerical models. This paper analyses the modeling of the hardening process in terms of temperature, phase transformations in the solid state and stresses in the elastic-plastic range. Currently, the use of artificial intelligence tools is increasing, both to make greater generalizations and to reduce possible errors in the numerical simulation process. It is possible to replace the mathematical model of phase transformations in the solid state with an artificial neural network (ANN). Such a substitution requires an ANN network that converts time series (temperature curves) into shares of phase transformations with a small training error. With an insufficient training level of the network, significant differences in stress values will occur due to the existing couplings. Long-Short-Term Memory (LSTM) networks were chosen for the analysis. The paper compares the differences in stress levels with two coupled models using a macroscopic model based on CCT diagram analysis and using the Johnson-Mehl-Avrami-Kolmogorov (JMAK) and Koistinen-Marburger (KM) equations, against the model memorized by the LSTM network. In addition, two levels of network training accuracy were also compared. Considering the results obtained from the model based on LSTM networks, it can be concluded that it is possible to effectively replace the classical model in modeling the phenomena of the heat treatment process.

In this work we report on fabrication of quantum wires and quantum point contacts from the modulation doped CdMgTe/Cd(Mn)Te structures, with the application of a high-resolution electron-beam lithography. We emphasize on methods which were not yet utilized for these substrate materials. In particular, we describe the so-called shallow-etching approach, which allows for the fabrication of quantum constrictions of a physical width down to 100 nm, which are characterized by the smoother confining potential as compared to the deep-etched devices. For that purpose, a single-line exposure mode of electron-beam lithography has been used. We demonstrate also, how to combine the etching of separating grooves with the thermal evaporation of metal side-gates into a single post-processing stage of a quantum point contact fabrication.

This article is an expanded version of the scientific reports presented at the International Conference on Semiconductor Nanostructures for Optoelectronics and Biosensors 2016 ICSeNOB2016, May 22–25, 2016, Rzeszow, Poland.