Abstract

Abstract
Irradiance received on the earth's surface is the main factor that affects the output power of solar PV plants, and is chiefly determined by the cloud distribution seen in a ground-based sky image at the corresponding moment in time. It is the foundation for those linear extrapolation-based ultra-short-term solar PV power forecasting approaches to obtain the cloud distribution in future sky images from the accurate calculation of cloud motion displacement vectors (CMDVs) by using historical sky images. Theoretically, the CMDV can be obtained from the coordinate of the peak pulse calculated from a Fourier phase correlation theory (FPCT) method through the frequency domain information of sky images. The peak pulse is significant and unique only when the cloud deformation between two consecutive sky images is slight enough, which is likely possible for a very short time interval (such as 1 min or shorter) with common changes in the speed of cloud. Sometimes, there will be more than one pulse with similar values when the deformation of the clouds between two consecutive sky images is comparatively obvious under fast changing cloud speeds. This would probably lead to significant errors if the CMDVs were still only obtained from the single coordinate of the peak value pulse. However, the deformation estimation of clouds between two images and its influence on FPCT-based CMDV calculations are terrifically complex and difficult because the motion of clouds is complicated to describe and model. Therefore, to improve the accuracy and reliability under these circumstances in a simple manner, an image-phase-shift-invariance (IPSI) based CMDV calculation method using FPCT is proposed for minute time scale solar power forecasting. First, multiple different CMDVs are calculated from the corresponding consecutive images pairs obtained through different synchronous rotation angles compared to the original images by using the FPCT method. Second, the final CMDV is generated from all of the calculated CMDVs through a centroid iteration strategy based on its density and distance distribution. Third, the influence of different rotation angle resolution on the final CMDV is analyzed as a means of parameter estimation. Simulations under various scenarios including both thick and thin clouds conditions indicated that the proposed IPSI-based CMDV calculation method using FPCT is more accurate and reliable than the original FPCT method, optimal flow (OF) method, and particle image yelocimetry (PIV) method.

Abstract
The amount of distributed generation (DG) the network can host depends on a number of parameters such as the characteristics of the generation units and their daily generation profiles, the characteristics of the network and its configuration, the hourly load profiles as well as national and regional grid code requirements. The high penetration of renewable energy sources (RES) in the distribution networks (DN), namely in medium voltage (MV) grids, may lead to reverse active power flows, to voltage rises and to an increase in voltage distortion due to the large use of power-electronic converters as generation interfaces with the grid, which may limit the hosting capacity (HC) of RES. This paper is intended to describe a new approach for identifying the HC for the integration of RES in electrical distribution systems. This is a planning tool based on the multi-period optimal power flow (MP-OPF) which aims to maximize the HC for DG under thermal and voltage constraints, involving also the verification of the harmonic voltage distortion via a set of current harmonic flow calculations, following a known current harmonic injection profile for each DG unit to be connected on grid. The case study shows that harmonic distortion limits may have substantial impacts on the allowable penetration of DG, for instance, due of the characteristics of DN and its configuration, the hourly load and generation profiles.

Abstract
A random-key genetic algorithm is an evolutionary metaheuristic for discrete and global optimization. Each solution is encoded as an array of n random keys, where a random key is a real number, randomly generated, in the continuous interval [0,1] A decoder maps each array of random keys to a solution of the optimization problem being solved and computes its cost. The algorithm starts with a population of p arrays of random keys. At each iteration, the arrays are partitioned into two sets, a smaller set of high-valued elite solutions and the remaining nonelite solutions. All elite elements are copied, without change, to the next population. A small number of random-key arrays (the mutants) are added to the population of the next iteration. The remaining elements of the population of the next iteration are generated by combining, with the parametrized uniform crossover of Spears and DeJong (On the virtues of parameterized uniform crossover. In: Proceedings of the fourth international conference on genetic algorithms, San Mateo, pp 230-236, 1991), pairs of arrays. This chapter reviews random-key genetic algorithms and describes an effective variant called biased random-key genetic algorithms.

Abstract
With the rapid growth in the use of digital platforms for the dissemination and expansion of a company’s business reach, it is vitally important that startup firms are firmly aware of the options when deciding whether to adopt a particular technology because they often have low resource availability, which reduces their margin for error. In order to help these companies to adopt Digital Marketing in a more secure way by knowing the most relevant factors that they can find as concerns the adoption of technologies, this study will analyze the factors that influence the adoption of technology, identifying them initially through the systematic literature review of similar scientific works. © Springer International Publishing AG 2018.

Abstract
The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system.