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At present, energy demands are mainly covered by the use of fossil fuels. The process of fossil fuel production increases pollution from oil extraction, transport to processing centers, treatment to obtain lighter fractions, and delivery and use by the final consumers. Such polluting circumstances are aggravated in the case of accidents involving fossil fuels. They are also linked to speculative markets. As a result, the trend is towards the decarbonization of lifestyles in advanced societies. The present paper addresses the problem of the optimal sizing of a hybrid renewable energy system for scheduling green hydrogen production. A local system fully powered by renewable energies is designed to obtain hydrogen from seawater. In order to monetize excess energy, the grid connection of the system is considered under realistic energy market constraints, designing an hourly purchasing strategy. This crucial problem, which has not been taken into account in the literature, is solved by the specific dispatch strategy designed. Several optimization methods have been used to solve this problem; however, the scatter search method has not previously been employed. In this paper, the problem is faced with a novel implementation of this method. The implementation is competitive in terms of performance when compared to, on the one hand, the genetic algorithm and differential evolution methods, which are well-known state-of-the-art evolutionary algorithms, and, on the other hand, the optimal foraging algorithm (OFA), a more recent algorithm. Furthermore, scatter search outperformed all other methods in terms of computational cost. This is promising for real-world applications that require quick responses. [ABSTRACT FROM AUTHOR]

Increasing urbanization and rising consumption rates are putting pressure on urban systems to efficiently manage Municipal Solid Waste (MSW). Waste collection, in particular, is one of the most challenging aspects of MSW management. Therefore, developing computer-aided tools to support decision-makers is crucial. In this paper, a Scatter Search algorithm is proposed to address the waste collection problem. The literature is relatively scarce in applying this algorithm, which has proven to be efficient in other routing problems, to real waste management problems. Results from real-world instances of an Argentine city demonstrate that the algorithm is competitive, obtaining, in the case of small instances, the same outcomes as those of an exact solver enhanced by valid inequalities, although requiring more computational time (as expected), and significantly improving the results of the latter for the case of larger instances, now requiring much less computational time. Thus, Scatter Search proves to be a competitive algorithm for addressing waste collection problems. [ABSTRACT FROM AUTHOR]

The economic development is the most influential factor on the power consumption of each country and each region, in long term estimation. In years of economic and financial crisis like the current one, a great variability of Gross Domestic Product (GDP) and Consumer Price Index (CPI) is observed. Particularly, CPI is sensitive to changes in the price of energy and the establishment of monetary policy. Therefore, the improvement of including CPI, in addition to GDP and population, as an explanatory variable to forecast the electricity consumption is investigated. For electricity companies it is important to have efficient prediction techniques to reduce uncertainty in the energy demand and obtain an optimal and realistic scheduling of the production of electricity. In pursuit of more objective conclusions, estimates are made using prediction methods of different nature, such as Multiple Linear Regression and Multiple Logarithmic Regression, which are classical statistical techniques, Support Vector Machine, which is a statistical learning technique, a Genetic Algorithm, which is an evolutionary computation techniques and an Artificial Neural Network, which is a machine learning technique. As a case study, the prediction of electricity demand in the Canary Islands is considered. It is of great interest for being an insulated electric system. The best prediction results are obtained with techniques which posses a greater capability to emulate nonlinear dependencies of the electricity demand in relation to population, GDP and CPI. [ABSTRACT FROM AUTHOR]