60 research outputs found
Evolutionary framework for multi-dimensional signaling method applied to energy dispatch problems in smart grids
In the smart grid (SG) era, the energy resource management (ERM) in power systems is facing an increase in complexity, mainly due to the high penetration of distributed resources, such as renewable energy and electric vehicles (EVs). Therefore, advanced control techniques and sophisticated planning tools are required to take advantage of the benefits that SG technologies can provide. In this paper, we introduce a new approach called multi-dimensional signaling evolutionary algorithm (MDS-EA) to solve the large-scale ERM problem in SGs. The proposed method uses the general framework from evolutionary algorithms (EAs), combined with a previously proposed rule-based mechanism called multi-dimensional signaling (MDS). In this way, the proposed MDS-EA evolves a population of solutions by modifying variables of interest identified during the evaluation process. Results show that the proposed method can reduce the complexity of metaheuristics implementation while achieving competitive solutions compared with EAs and deterministic approaches in acceptable times.The present work was done and funded in the scope of the projects: Project NetEffiCity (ANI—P2020 18015), and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013; Sustainability Fund CONACYT-SENER by Consejo Nacional de Ciencia y Tecnolog´ıa (CONACYT) and the National Center of Innovation in Energy (CEMIE-Eolico, Project No. 206842).info:eu-repo/semantics/publishedVersio
Differential evolution strategies for large-scale energy resource management in smart grids
Smart Grid (SG) technologies are leading the modifications of power grids worldwide. The Energy Resource Management (ERM) in SGs is a highly complex problem that needs to be efficiently addressed to maximize incomes while minimizing operational costs. Due to the nature of the problem, which includes mixed-integer variables and non-linear constraints, Evolutionary Algorithms (EA) are considered a good tool to find optimal and near-optimal solutions to large-scale problems. In this paper, we analyze the application of Differential Evolution (DE) to solve the large-scale ERM problem in SGs through extensive experimentation on a case study using a 33-Bus power network with high penetration of Distributed Energy Resources (DER) and Electric Vehicles (EVs), as well as advanced features such as energy stock exchanges and Demand Response (DR) programs. We analyze the impact of DE parameter seing on four state-of-the art DE strategies. Moreover, DE strategies are compared with other well-known EAs and a deterministic approach based on MINLP. Results suggest that, even when DE strategies are very sensitive to the seing of their parameters, they can find beer solutions than other EAs, and near-optimal solutions in acceptable times compared with a MINLP approach.The present work was done and funded in the scope of the projects: Sustainability Fund CONACYT-SENER by Consejo Nacional de Ciencia y Tecnología (CONACYT) and the National Center of Innovation in Energy (CEMIE-Eolico); H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794) and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCTinfo:eu-repo/semantics/publishedVersio
Monitoring attentional processes for intelligent channelling of educational tasks
Aims:
- Detection of attention: Map a lexicon of body postures to binarizedattentionallevels (Experiment I).
- Attribution of attention: Identify postural features leading to appreciation of attention by third parties (e.g. educators) (Experiment II).Panel ACE.Ibero-American Science and Technology Education Consortium (ISTEC
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Inferring multiple coffee flowerings in Central America using farmer data in a probabilistic model
Coffee (Coffea arabica L.) is a climate-sensitive crop; rainfalls may trigger flowering event occurrences, and extreme rainfall during a flowering day can cause considerable yield reductions. Multiple flowering events can occur in the span of 12 months; the number varies from year to year. This paper introduces a Bayesian network model capable of inferring coffee flowering events in coffee areas in the Pacific Region of Central America based on observed data for coffee flowering and precipitation. The model structure was determined based on expert knowledge, and the model parametrization was learned from 53 years of data registered in the region. Data from four farms in the region were used for model validation. The model's performance in the inference of flowering intensity was good (spherical payoff of 0.78 out of maximal 1.00), and the model was able to depict expected behaviors for single and multiple flowerings. Further, comprehensive new details on the dynamics of multiple flowerings within a crop season were obtained, e.g., that a large flowering event tends to occur more quickly (8 to 10 days) after rain than a small flowering (10 to 13 days). We believe that this Bayesian network model has the potential to evolve and support the development of agricultural index-based insurance to deal with yield losses due to extreme rainfall during flowering. The use of longer farm records for model building may also serve to increase farmers' trust in the reliability of the tool
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