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This paper addresses the problem of the optimal location and sizing of photovoltaic (PV) sources in direct current (DC) electrical networks considering time-varying load and renewable generation curves. To represent this problem, a mixed-integer nonlinear programming (MINLP) model is developed. The main idea of including PV sources in the DC grid is minimizing the total greenhouse emissions produced by diesel generators in isolated areas. An artificial neural network is employed for short-term forecasting to deal with uncertainties in the PV power generation. The general algebraic modeling system (GAMS) package is employed to solve the MINLP model by using the CONOPT solver that works with mixed and integer variables. Numerical results demonstrate important reductions of harmful gas emissions to the atmosphere when PV sources are optimally integrated (size and location) to the DC grid.

The peanut (Arachys hypogaea) is a plant of the Fabaceae family (legumes), as are chickpeas, lentils, beans, and peas. It is originally from South America and is used mainly for culinary purposes, in confectionery products, or as a nut as well as for the production of biscuits, breads, sweets, cereals, and salads. Also, due to its high percentage of fat, peanuts are used for industrialized products such as oils, flours, inks, creams, lipsticks, etc. According to the Food and Agriculture Organization (FAO) statistical yearbook in 2016, the production of peanuts was 43,982,066 t, produced in 27,660,802 hectares. Peanuts are grown mainly in Asia, with a global production rate of 65.3%, followed by Africa with 26.2%, the Americas with 8.4%, and Oceania with 0.1%. The peanut industry is one of the main generators of agroindustrial waste (shells). This residual biomass (25–30% of the total weight) has a high energy content that is worth exploring. The main objectives of this study are, firstly, to evaluate the energy parameters of peanut shells as a possible solid biofuel applied as an energy source in residential and industrial heating installations. Secondly, different models are analysed to estimate the higher heating value (HHV) for biomass proposed by different scientists and to determine which most accurately fits the determination of this value for peanut shells. Thirdly, we evaluate the reduction in global CO2 emissions that would result from the use of peanut shells as biofuel. The obtained HHV of peanut shells (18.547 MJ/kg) is higher than other biomass sources evaluated, such as olive stones (17.884 MJ/kg) or almond shells (18.200 MJ/kg), and similar to other sources of biomass used at present for home and industrial heating applications. Different prediction models of the HHV value proposed by scientists for different types of biomass have been analysed and the one that best fits the calculation for the peanut shell has been determined. The CO2 reduction that would result from the use of peanut shells as an energy source has been evaluated in all production countries, obtaining values above 0.5 ‰ of their total emissions.

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This study aims to highlight the contribution made by different international institutions in the field of urban generation of renewable energy, as a key element to achieve sustainability. This has been possible through the use of the Scopus Elsevier database, and the application of bibliometric techniques through which the articles content published from 1977 to 2017 has been analysed. The results shown by Scopus (e.g., journal articles and conferences proceedings) have been taken into account for further analysis by using the following search pattern (TITLE-ABS-KEY ({Renewable energy} AND ({urban} OR ({cit*})). In order to carry out this study, key features of the publications have been taken into consideration, such as type of document, language, thematic area, type of publication, and keywords. As far as keywords are concerned, renewable energy, sustainability, sustainable development, urban areas, city, and energy efficiency, have been the most frequently used. The results found have been broken down both geographically and by institution, showing that China, the United States, the United Kingdom, Italy, Germany and India are the main research countries and Chinese Academy of Sciences, Ministry of Education China and Tsinghua University the major contributing institutes. With regard to the categories, Energy, Environmental Sciences, and Engineering are positioned as the most active categories. The scientific community agrees that the study of the renewable energy generation in cities is of vital importance to achieve more sustainable cities, and for the welfare of a growing urban population. Moreover, this is in line with the energy policies adopted by most of developed countries in order to mitigate climate change effects.

This paper presents an optimization model for the optimal placement and sizing of wind turbines, considering their reactive power capacity, wind speed, and demand curves. The optimization model is nonlinear and is focused on minimizing power losses in AC distribution networks. Also, paired wind turbine and power conversion systems are treated via chargeability factor η at the peak hour. This factor represents the percentage of usage of the power conversion system in the nominal wind speed conditions, and allows to support reactive power dynamically during all periods of the day as a function of the distribution system requirements. In addition, an artificial neural network is used for short-term forecasting to deal with uncertainties in wind power generation. We assume that the number of wind power distributed generators could be from zero to three generators integrated into the system, considering unit power factors and reactive power injections to follow up the effect of reactive power compensation in the daily operation. The General Algebraic Modeling System (GAMS) is employed to solve the proposed optimization model.