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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.

A reduced-order, “grey-box” model of an active distribution network, intended for dynamic simulations of the transmission system, is derived. The network hosts inverter-based generators as well as static and motor loads, whose dynamic parameters are affected by uncertainty. This issue is addressed using Monte-Carlo simulations. The parameters of the equivalent are adjusted to match as closely as possible the average of the randomized responses, while their dispersion is accounted for through the weights of the weighted least-square minimization. A procedure is used to remove from the identification the parameters with negligible impact. To avoid over-fitting, the equivalent is tuned for multiple large-disturbance simulations. A recursive procedure is used to select the smallest possible subset of disturbances involved in the least-square minimization. Simulation results with a 75-bus MV test-system are reported. They show that the equivalent is able to reproduce with good accuracy the discontinuous controls of inverter-based generators, such as reactive current injection and tripping.

It's not only the carbon in the trees Forest loss affects climate not just because of the impacts it has on the carbon cycle, but also because of how it affects the fluxes of energy and water between the land and the atmosphere. Evaluating global impact is complicated because deforestation can produce different results in different climate zones, making it hard to determine large-scale trends rather than more local ones. Alkama and Cescatti conducted a global assessment of the biophysical effects of forest cover change. Forest loss amplifies diurnal temperature variations, increases mean and maximum air temperatures, and causes a significant amount of warming when compared to CO 2 emission from land-use change. Science , this issue p. 600

Due to resource constraints in wireless sensor networks (WSNs), energy consumption and networks' lifetime are considered significant challenges. Because sensors have a tiny battery and cannot be charged again. In WSN, collected data is usually transferred to the Base station (BS) directly or hop-by-hop. Therefore, load balancing and routing are one of the main issues in the WSN. This paper proposes a new routing scheme with load-balancing capability using the Markov Model (MM) and the Artificial Bee Colony (ABC) algorithm. LEACH algorithm is used to maintain load balancing between Cluster Heads (CHs). Then the Markov Model and the Artificial Bee Colony (MMABC) algorithm were used to find the best candidate nodes of each cluster to be turned into a CH. The simulation results in MATLAB software demonstrated that the proposed method surpasses the compared methods in terms of energy efficiency, number of alive nodes, and the number of delivered packets to BS and CH. European Union [861219]; Marie Curie Actions (MSCA) [861219] Funding Source: Marie Curie Actions (MSCA) This study has been conducted under the project Mobility and Training for beyond 5G ecosystems (MOTOR5G)'. The project has received funding from the European Union's Horizon 2020 program under the Marie Sklodowska Curie Actions (MSCA) Innovative Training Network (ITN) having grant agreement No. 861219.

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.

In a stable external environment, a slow crystallization rate fosters a stable lattice of pure “black”-phase perovskite, while post treatment at the grain boundaries enhance overall grain stability, contributing to long-term stability.