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Abstract This paper aims to present a feasibility study of the innovative plant for methanol synthesis from carbon dioxide-sequestered by fossil fuel power plant and hydrogen, which is produced by water electrolyzer employing the over-production on the electrical grid. The thermo-economic analysis is performed in the framework of the MefCO2 H2020 EU project and it is referred to the German economic scenario, properly taking into account the real market costs and cost functions for different components of the plant. Three different plant capacities for methanol production (4000 10,000 and 50,000 ton/year) have been investigated, assuming an average cost for electrical energy to feed electrolysers and analyzing the influence of the most significant parameters (oxygen selling option, methanol selling price and electrolysers’ capital cost) on the profitability of the plant. The analysis has been performed in W-ECoMP, software for the thermo-economic analysis and plant optimization developed by the University of Genoa.

This paper presents the design of an extended Kalman filter (EKF) as a state-of-charge (SoC) estimator for a lithium–titanate (LTO) battery cell. The design is based on a Thevenin model of the equivalent battery electrical circuit with two parallel resistive-capacitive (RC) elements for the emulation of electrolyte polarization effects. The effectiveness of the proposed SoC estimator is verified through computer simulations on the comprehensive nonlinear battery simulation model featuring battery parameter vs. SoC variations, which is subject to highly dynamic loading regimes.

Abstract Some new structures of thin-film solar converters (SC) based on heterojunctions (HJ) with intermediate semiconductor layers are suggested. Thin protective layers and a quasi-electric field incorporated into the space charge region (SCR) prevent cross-diffusion of HJ components and increase efficiency of charge carrier separation. They also decrease the diode dark current and provide high stability of the converter parameters. Thin (∼ 0.1 μ M) (CdSe) x (ZnTe) 1− x or Zn x Cd 1− x Se layers were used as graded band-gap layers. They were places between a photosensitive II-VI-compound (CdTe, CdSe, CdSe x Te 1− x ) base layer and the transparent Cu 1.8 S layer. The above structures were prepared by vacuum closed space sublimation. The properties of these compounds were studied by electron microscopy and X-ray photoelectron spectroscopy (XPS) with ion etching. The photoelectron properties of structures such as Cu 1.8 S/(CdSe) x )(ZnTe) 1− x /CdSe are presented in detail. The manufacturing technology for the integrated solar batteries based on CdTe, CdSe, and CdSe x Te 1− x compounds was developed. The solar cell parameters under low illumination intensities are comparable to those of solar batteries based on c-Si and a-Si. The competitiveness of the polycrystalline thin-film SC is due to ease and low cost of fabrication (as compared with c-Si and a-Si) and also to the extended photosensitivity range (as compared to a-Si).

Abstract Offshore wind energy is a seminal technology to achieve the goals set for renewable energy deployment. However, today's offshore wind energy projects are mostly not yet sufficiently competitive. The optimization of offshore wind turbine substructures with regard to costs and reliability is a promising approach to increase competitiveness. Today, interdisciplinary analyses considering sophisticated engineering models and their complex economic effects are not widespread. Existing approaches are deterministic. This research gap is addressed by combining an aero-elastic wind turbine model with an economic viability model for probabilistic investment analyses. The impact of different monopile designs on the stochastic cost-efficiency of an offshore wind farm is investigated. Monopiles are varied with regard to diameters and wall thicknesses creating designs with increased lifetimes but higher capital expenditures (durable designs) and vice versa (cheaper designs). For each substructure, the aero-elastic wind turbine model yields distributions for the fatigue lifetime and electricity yield and different capital expenditures, which are applied to the economic viability model. For other components, e.g. blades, constant lifetimes and costs are assumed. The results indicate that the gain of increased stochastic lifetimes exceeds the benefit of reduced initial costs, if the overall lifetime is not governed by other turbine components' lifetimes.

Abstract Decarbonizing world economies implies the deployment of “green technologies”, meaning a renovation of the energy sector towards using renewable sources and zero emission transport technologies. This renovation will require huge amounts of raw materials, some of them with high supply risks. To assess such risks a new methodology is proposed, identifying possible bottlenecks of future demand versus geological availability. This has been applied to the world development of wind power, solar photovoltaic, solar thermal power and passenger electric vehicles for the 2016–2050 time period under a business as usual scenario considering the impact on 31 different raw materials. As a result, 13 elements were identified to have very high or high risk, meaning that these could generate bottlenecks in the future: cadmium, chromium, cobalt, copper, gallium, indium, lithium, manganese, nickel, silver, tellurium, tin and zinc. Tellurium, which is mostly demanded to manufacture solar photovoltaic cells, presents the highest risk. To overcome these constraints, measures consisting on improving recycling rates from 0.1% to 4.6% per year could avoid material shortages or restrictions in green technologies. For instance, lithium recycling rate should increase from 1% to 4.8% in 2050. This study aims to serve as a guideline for developing eco-design and recycling strategies.

This paper investigates the impact of plug-in electric vehicle (EV) integration on the power systems scheduling and energy cost. An intermediary entity, the EV aggregator, participates in the market on behalf of the EV owners by optimally self-scheduling under the price-taking approach. Through detailed rolling simulations for a year and different EVs’ penetration scenarios at a large insular power system, this work highlights the different impact of direct and smart charging on power system scheduling and energy costs, the limitations of the price-taking approach, which is widely used in self-scheduling models, and the difference in system value and market value that smart charging adoption creates in restructured markets under the marginal pricing rule.

Abstract The paper presents a model-based approach to supporting battery selection for a fuel cell (FC)-based auxiliary power unit (APU). It is introduced to a case study of electrical power production and consumption management in a truck anti-idling application of a diesel-powered FC-based APU, a system under development in FCGEN, a FCH JU European project of the FP7 program. With fuel cell and related technologies increasingly competing with others in the market, they need to form complete systems with matching and well-balanced components to enable using the technology to its best. Within the whole system, the battery, serving as an energy buffer, represents a medium-cost element, but it affects the operating parameters importantly. Within the scope of this study, a purpose-oriented model of the diesel powered FC-based system is developed together with a realistic load scenario for the comparison of three batteries. The battery size and type are investigated and discussed in the light of the simulation results.

We demonstrate the direct electrochemical conversion of CO2 to CO using solid state Ni–N–C carbon catalysts characterized by a coordinative molecular Ni–Nx active moiety at industrial current densities of up to 700 mA cm−2 with faradaic efficiencies superior to those of the state-of-the-art AgOx electrocatalysts.

Abstract The energy efficiency is one of the most important issues nowadays; the problem with the buildings heating is especially relevant for Ukraine. The aim of the paper is to develop a convenient tool building energy performance analysis based on regression model for internal air temperature prediction, depending on a number of internal and external influential factors. The external climatic factors, such as outside air temperature, wind speed and direction, solar heat gains depending on building fenestration surfaces orientation, are considered. Internal factors include heating load, number of floors, air exchange rate etc. In order to achieve the goal, a room dynamic simulation model is created in the EnergyPlus software. A number of simulations are carried out based on the created building energy model . The individual and aggregate selected factors influence on inside air temperature change is considered. The general structure of the multivariate nonlinear regression model for inside air temperature determination is analyzed and selected. Constant coefficients are obtained for each selected influencing factor, and verification of the received nonlinear regression model is performed based on simulation data using January climatic data from the IWEC file. The adequacy of the obtained regression model is estimated by the corrected determination coefficient (R 2 = 0.981) and Fisher's criterion (F = 1324.3), which indicates the high accuracy of the obtained multivariate nonlinear regression. The proposed approach for regression model creation can be used for other architectural and thermal properties of building envelope.