Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation techniques, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES while alleviating grid-connect issues. Microgrids are interconnected RESs and electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate independently and be grid-connected. The paper presents a review and performance assessment of renewable energy-based microgrids under various operating scenarios in stand-alone, grid-connected, and transitioning modes of operation. Fault occurrences, an increase in micro-source generation, a load increase, and the sudden disconnection of a micro-source are some of the simulated scenarios. Microgrid network components’ performance, such as the bidirectional DC-DC converter and energy storage system (ESS), was evaluated. The simulated microgrid architecture includes a small hydroelectric plant, wind farm, and ESS. The work provides valuable information to energy stakeholders on the performance of microgrids in low-voltage distribution networks. The microgrid is coupled to a low-voltage distribution network (0.415 kV) via a PCC. The system under investigation is modeled and simulated using MATLAB/Simulink. From the simulation analysis, the fault effect was felt on the utility and did not escalate to the microgrid side during stand-alone operation. Power quality issues, such as voltage rise, are some of the challenges identified during the transition from one mode of operation to another. However, the energy storage system responds to disturbances and maintains system stability. The originality of this paper is based on evaluating different modes of operation of microgrids and comparing system performances under various operating conditions.
Numerical simulation is a widely used tool for studying CO2 storage in porous media. It enables the representation of trapping mechanisms and CO2 retention capacity. The complexity of the involved physicochemical phenomena necessitates multiphase flow, accurate fluid and rock property representation, and their interactions. These include CO2 solubility, diffusion, relative permeabilities, capillary pressure hysteresis, and mineralization, all crucial in CO2 trapping during carbon storage simulations. Experimental data is essential to ensure accurate quantification. However, due to the extensive data required, modeling under uncertainty is often needed to assess parameter impacts on CO2 trapping and its interaction with geological properties like porosity and permeability. This work proposes a framework combining laboratory data and stochastic parameter distribution to map uncertainty in CO2 retention over time. Published data representing solubility, residual trapping, and mineral trapping are used to calibrate prediction models. Geological property variations, like porosity and permeability, are coupled to quantify uncertainty. Results from a saline sandstone aquifer model demonstrate significant variation in CO2 trapping, ranging from 17% (P10 estimate) to 56% (P90), emphasizing the importance of considering uncertainty in CO2 storage projects. Quadratic response surfaces and Monte Carlo simulations accurately capture this uncertainty, resulting in calibrated models with an R-squared coefficient above 80%. In summary, this work provides a practical and comprehensive framework for studying CO2 retention in porous media, addressing uncertainty through stochastic parameter distributions, and highlighting its importance in CO2 storage projects.
This article presents the opportunities for constructing a global data base picturing underlying trends that drive global climate change. Energy-related CO2 emissions currently represent the key impact on climate change and thus become here the object of deep, long-term and historiographic analysis. In order to embrace all involved domains of technology, energy economy, fuel shares, economic efficacity, economic structure and population, a “Global Change Data Base” (GCDB) is suggested, based on earlier worldwide accepted data repositories. Such a GCDB works through regressions and statistical analysis of time series of data (on extensive magnitudes such as energy demand, population or Gross Domestic Product, GDP) as well as generation of derived data such as quotients of the former, yielding intensive magnitudes that describe systems and their structural properties. Moreover, the GCDB sets out to compute the first and second time derivatives of said magnitudes (and their percentual shares) which indicate new long-term developments already at very early phases. The invitation to participate in this foresight endeavour is extended to all readers. First preliminary GCDB results quantitatively portray the evolutionary structural global dynamics of economic growth, sectoral economic shifts, the shifts within energy carriers in various economic sectors, the ongoing improvements of energy intensity and energy efficiency in many economic sectors, and the structural changes within agricultural production and consumption systems.
This paper focuses a novel non-isolated coupled inductor based DC-DC converter with excessive VG (voltage gain) is analyzed with a state-space modeling technique. It builds up of using three diodes, three capacitors, an inductor and CI (coupled inductor). The main switch S is turn on due to body diode and voltage stress is reduced at the switch S by using diode D1 and Capacitor C1. This paper focuses on design modelling, mathematical calculations and operation principle of DC-DC converter is discussed with state-space modelling technique. The performance has been presented for two different voltages for EV applications, i.e., 12 V, 48 V as input voltages with a high step-up outputs of 66 V and 831.7 V respectively. The converter stability is studied and determined the bode plot along with simulation performance results which are carried out using MATLAB R2022B.
Solar energy, as a clean source of energy, plays a relevant role in this much desired (r)evolution. When talking about photovoltaics, despite the multiple studies on parameters that affect the panels operation, concrete knowledge on this matter is still in an incipient stage and precise data remains dispersed, given the mutability of outer factors beyond technology-related properties, hence the difficulties associated with exploration. Wind is one of them. Wind loads can affect the temperature of photovoltaics, whose efficiency is reduced when higher temperatures are reached. The viability of wind as natural cooling mechanism for solar plants and its influence on their electrical energy production is studied in this research work. Some appropriate results were achieved: depending on the module temperature prediction model used and on the photovoltaic technology in question, solar panels are foreseen to be up to approximately 3% more productive for average wind speeds and up to almost 7% more productive for higher speeds. Taking into consideration that wind speed values were collected in the close vicinity of the modules, these results can be proven to be even higher. That being said, this article contributes with accurate insights about wind influence on electrical energy production of solar plants.
