• Energy Research

This paper reviews the scientific production of renewable energies, namely, solar, wind, biomass, hydropower and geothermal, from 1979 to 2009. The production of all the countries in the world is analysed, paying particular attention to renewable energies and research institutions. The production of scientific research for each type of energy is represented on world maps to show the degree of relationship between this research and the resources of these energies. It is observed that biomass is the most studied, both by number of publications, with 56% of the publications on renewable energy, and by geographical distribution. The next in importance by number of publications is solar energy (26%). The countries investigating solar energy, however, are not necessarily those with the greatest availability of this resource. Wind is the third positioned in publication (11%). Wind is being investigated by countries that most have implemented this type of energy production. Hydro and geothermal energies are also investigated by countries with great abundance of this resource. It is observed that research on renewable energy is highly concentrated in a few countries (12 or 14, depending on the energy type), accounting for between 70 and 80% of scientific production. The role of the USA as a leader in research in all renewable energies studies is emphasised. NASA is the leading institution for solar and wind energy, the Chinese Academy of Sciences leads in hydropower and biomass, and the U.S. Geological Survey leads in geothermal energy.

Microgrids are local energy production and distribution networks that can operate independently when disconnected from the main power grid thanks to the integration of power generation systems, energy storage units and intelligent control systems. However, despite their advantages, the optimal energy management of real microgrids remains a subject that requires further investigation. Specifically, an effective management of microgrids requires managing a large number of electrical variables related to the power generated by the microgrid’s power supplies, the power consumed by the loads and the aspects of power quality. This study analyzes how we can monitor different variables, such as the active power, reactive power, power factor, total harmonic distortion and frequency in the loads of a microgrid, using high-precision power meters. Our empirical study, conducted using a functional microgrid comprising a hybrid wind–solar power system and several household appliances, demonstrates the feasibility of using low-cost and high-performance power meters with IoT functionality to collect valuable power quality and energy consumption data that can be used to control the microgrid operation.

Abstract The decentralization of electrical power production is conducive to a more effective and harmonious use of energy resources. For this reason, photovoltaic grid-connected plants (PVGCPs) as well as other renewable energy sources have come into the spotlight in recent years since they improve the supply of electrical power to the grid. The optimization of PVGCP design has been previously addressed in terms of electrical losses with successful results. However, PVGCP performance can be further enhanced if other characteristics, such as power capacity, are taken into consideration. This paper focuses on the optimization of the design of photovoltaic plants with solar tracking. The research described had the following two objectives: (i) the maximization of power capacity; (ii) the minimization of electrical losses. This problem was solved with multi-objective evolutionary algorithms, which have proved to be powerful optimization techniques that are useful for a wide range of objectives. This paper focuses on the NSGA-II and SPEA2, two well-known multi-objective algorithms, and describes how they were used to optimize PVGCPs. The resulting sets of solutions provide the flexibility and adaptability needed to build a PVGCP. These algorithms were thus found to be an effective tool for enhancing PVGCP performance.

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of generation, as most renewable energy resources depend on the climate, which is why their use requires complex design, planning and control optimization methods. Fortunately, the continuous advances in computer hardware and software are allowing researchers to deal with these optimization problems using computational resources, as can be seen in the large number of optimization methods that have been applied to the renewable and sustainable energy field. This paper presents a review of the current state of the art in computational optimization methods applied to renewable and sustainable energy, offering a clear vision of the latest research advances in this field.

Voltage regulation is an important task in electrical engineering for controlling node voltages in a power network. A widely used solution for the problem of voltage regulation is based on adjusting the taps in under load tap changers (ULTCs) power transformers and, in some cases, turning on Flexible Alternating Current Transmission Systems (FACTS), synchronous machines or capacitor banks in the substations. Most papers found in the literature dealing with this problem aim to avoid voltage drops in radial networks, but few of them consider power losses or meshed networks. The aim of this paper is to present and evaluate the performance of several multi-objective algorithms, including hybrid approaches, in order to minimize both voltage deviation and power losses by operating ULTCs located in high voltage substations. In particular, a well-known multi-objective algorithm, PAES, is used for this purpose. PAES finds a set of solutions according to Pareto-optimization concepts. Furthermore, this algorithm is hybridized with simulated annealing and tabu search to improve the quality of the solutions. The implemented algorithms are evaluated using two test networks, and the numerical results are analyzed with two metrics often used in the multi-objective field. The results obtained demonstrate the good performance of these algorithms.