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In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions inside each subpopulation. We introduce here a further extension of Hy3, called Hy4, that uses 16 islands arranged in a hypercube of four dimensions. Thus, two new faces with different exploration/exploitation search capabilities are added to the search performed by Hy3. We analyze the importance of running a synchronous versus an asynchronous version of the models considered. The results indicate that the proposed Hy4 model overcomes the Hy3 performance because of its improved balance between exploration and exploitation that enhances the search. Finally, we also show that the async Hy4 model scales better than the sync one.

a b s t r a c tDespite the advantages that power plants based on renewable energies offer, there are some restrictions to the social acceptance of these facilities. One of these restrictions is the visual impact that large power plants may generate on people. This paper presents a new methodology for ranking the feasible places in a zone for the construction of new photovoltaic (PV) power plants according to their visibility. The methodology is based on the fuzzy viewshed and the distance decay methods, which enable to calculate the maximum number of hours in a mean day in which the new PV plant may be viewed by each possible observer. This number is related to the inhabitants in the zone, the size of the plant, the possible observers from paths and roads, and their distance to the PV plant. The proposed methodology is implemented in a Geographical Information System which allows the presentation of visual results that help to identify the best areas in the zone under study. This methodology can be useful to local authorities who have to authorize the installation of the new power plant, or investors who are trying to find the best locations from the point of view of visual impact.

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A smart meter enables electric utilities to get detailed insights into their customer needs, allowing them to offer tailored products and services, and to succeed in an increasingly competitive market. While in an ideal world companies would treat every customer as an individual, in practice this is rather difficult. For this reason, companies usually have to target smaller groups of customers that are similar. There are several ways of tackling this matter and finding the right approach is a key to success. Therefore, in this study we introduce the electricity demand signature, a novel approach to characterize and cluster electricity customers based on their demand habits. We test our proposal using the electricity demand of 64 buildings in Andalusia, Spain, and compare our results to the state-of-the-art. The results show that our proposal is useful for clustering customers in a meaningful way, and that it is an easy and friendly representation of the behavior of a customer that can be used for further analysis.