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- Improving energy efficiency in smart-houses by optimizing electrical loads managementPublication . Cabrita, Cristiano Lourenço; Monteiro, Jânio; Cardoso, PedroIn this work, the Genetic Algorithm is explored for solving a predictive based demand side management problem (a combinatorial optimization problem) and the main measures lbr performance evaluation are evaluated. In this context, we propose a smart energy scheduling approach for household appliances in real-time to achieve minimum consumption costs and a reduction in peak load. We consider a scenario of selfconsumption where the surplus from local power generation can be sold to the grid, and the existence of appliances that can be shiftable from peak hours to off-peak hours. Results confirm the importance of the tuning procedure and the structure of the genome and algorithm's operators determine the performance of such type of meta-heuristics. This fact is more decisive when there are several operational constraints on the system, as for example short-term optimal scheduling decision, time constraints and power limitations. Details about the scheduling problem, comparison strategies, metrics, and results are provided.
- On a new method to design solar photovoltaic systems in renewable energy communities: The case of Culatra Island (Ria Formosa, Portugal)Publication . Ewart, M.; Santos, J.; Pacheco, A.; Monteiro, Jânio; Sequeira, ClaudiaIslands must reach sustainable lifestyles by improving resources management and by getting accustomed to renewable energy sources. Culatra, a small Portuguese island, is actively increasing renewable energy penetration into local processes in order to be the first 100% sustainable Portuguese territory by 2030. Based on the electric consumption of Culatra, the objective of the present study is to project a photovoltaic unit composed of several sub-fields, each with a distinct orientation, in order to increase the self-consumption ratio while at the same time reducing the surplus of energy production, assessed by the self-sufficiency ratio criteria, resulting in a lower levelised cost of energy of the power system. To achieve this, MATLAB's implementation of a genetic algorithm was used to find the optimised set of orientations for a given load profile. The results indicate that it is possible to optimise the photovoltaic plant to reach a more continuous electricity generation through daytime, reducing the storage needs and increasing the value of photovoltaic systems. The method can be extended to other locations or demand curves, assisting on comparing different energy management strategies, and their respective advantages and disadvantages.