Percorrer por autor "Torres, Santiago"
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- An innovative optimization approach for energy management of a microgrid systemPublication . Amoura, Yahia; Pereira, Ana I.; Lima, José; Ferreira, Ângela P.; Boukli-Hacene, Fouad; Torres, SantiagoThe local association of electrical generator including renewable energies and storage technologies approximately installed to the client made way for a small-scale power grid called a microgrid. In certain cases, the random nature of renewable energy sources, combined with the variable pattern of demand, results in issues concerning the sustainability and reliability of the microgrid system. Furthermore, the cost of the energy coming from conventional sources is considering as matter to the private consumer due to its high fees. An improved methodology combining the simplex-based linear programming with the particle swarm optimisation approach is employed to implement an integrated power management system. The energy scheduling is done by assuming the consumption profile of a smart city. two scenarios of energy management have been suggested to illustrate the behaviour of cost and gas emissions for an optimised energy management. The results showed the reliability of the energy management system using an improvemed approach in scheduling of the energy flows for the microgrid producers, limiting the utility’s cost versus an experiment that had already been done for a similar system using the identical data. The outcome of the computation identified the ideal set points of the power generators in a smart city supplied by a microgrid, while guaranteeing the comfort of the customers i.e without intermetency in the supply, also, reducing the emissions of greenhouse gases and providing an optimal exploitation cost for all smart city users. Morover, the proposed energy management system gave an inverse relation between economic and environmental aspects, in fact, a multi-objective optimization approach is performed as a continuation of the work proposed in this paper
- Combined optimization and regression machine learning for solar Irradiation and wind speed forecastingPublication . Amoura, Yahia; Torres, Santiago; Lima, José; Pereira, Ana I.Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.
- Hybrid optimisation and machine learning models for wind and solar data predictionPublication . Amoura, Yahia; Torres, Santiago; Lima, José; Pereira, Ana I.The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources. This article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used.
- Multi-objective optimal sizing of an AC/DC grid connected microgrid systemPublication . Amoura, Yahia; Pedroso, André; Ferreira, Ângela P.; Lima, José; Torres, Santiago; Pereira, Ana I.Considering the rising energy needs and the depletion of conventional energy sources, microgrid systems combining wind energy and solar photovoltaic power with diesel generators are promising and considered economically viable for usage. To evaluate system cost and dependability, optimizing the size of microgrid system elements, including energy storage systems connected with the principal network, is crucial. In this line, a study has already been performed using a uni-objective optimization approach for the techno-economic sizing of a microgrid. It was noted that, despite the economic criterion, the environmental criterion can have a considerable impact on the elements constructing the microgrid system. In this paper, two multi-objective optimization approaches are proposed, including a non-dominated sorting genetic algorithm (NSGA-II) and the Pareto Search algorithm (PS) for the eco-environmental design of a microgrid system. The k-means clustering of the non-dominated point on the Pareto front has delivered three categories of scenarios: best economic, best environmental, and trade-off. Energy management, considering the three cases, has been applied to the microgrid over a period of 24 h to evaluate the impact of system design on the energy production system’s behavior.
- The role of anthropogenic habitats in freshwater mussel conservationPublication . Sousa, Ronaldo; Halabowski, Dariusz; Labecka, Anna M.; Douda, Karel; Aksenova, Olga V.; Bespalaya, Yulia V.; Bolotov, Ivan N.; Geist, Juergen; Jones, Hugh A.; Konopleva, Ekaterina; Klunzinger, Michael W.; Lasso, Carlos A.; Lewin, Iga; Liu, Xiongjun; Lopes-Lima, Manuel; Mageroy, Jon; Mlambo, Musa; Nakamura, Keiko; Nakano, Mitsunori; Österling, Martin E.; Pfeiffer, John; Prié, Vincent; Paschoal, Lucas R.P.; Riccardi, Nicoletta; Santos, Rogério; Shumka, Spase; Smith, Allan K.; Son, Mikhail O.; Teixeira, Amílcar; Thielen, Frankie; Torres, Santiago; Varandas, Simone; Vikhrev, Ilya V.; Wu, Xiaoping; Zieritz, Alexandra; Nogueira, Joana GarridoAnthropogenic freshwater habitats may provide undervalued prospects for long-term conservation as part of species conservation planning. This fundamental, but overlooked, issue requires attention considering the pace that humans have been altering natural freshwater ecosystems and the accelerated levels of biodiversity decline in recent decades. We compiled 709 records of freshwater mussels (Bivalvia, Unionida) inhabiting a broad variety of anthropogenic habitat types (from small ponds to large reservoirs and canals) and reviewed their importance as refuges for this faunal group. Most records came from Europe and North America, with a clear dominance of canals and reservoirs. The dataset covered 228 species, including 34 threatened species on the IUCN Red List. We discuss the conservation importance and provide guidance on how these anthropogenic habitats could be managed to provide optimal conservation value to freshwater mussels. This review also shows that some of these habitats may function as ecological traps owing to conflicting management practices or because they act as a sink for some populations. Therefore, anthropogenic habitats should not be seen as a panacea to resolve conservation problems. More information is necessary to better understand the trade-offs between human use and the conservation of freshwater mussels (and other biota) within anthropogenic habitats, given the low number of quantitative studies and the strong biogeographic knowledge bias that persists.
- A statistical estimation of wind data generation in the municipality of Bragança, PortugalPublication . Amoura, Yahia; Lima, José; Torres, Santiago; Pereira, Ana I.The existing wind energy potential in Portugal makes way for developing electrical energy in the northern region. In this work, wind speed data were statistically investigated using Weibull distribution to identify the characteristics of converting wind energy in Serra da Nogueira mountain in the Municipality of Braganca. An hourly wind speed time series data set from January 2002 to December 2021 have been exported from OPEN-METEO online platform after reliability data was proved through a correlation study with real data. The Weibull parameters including form K and scale C factors, frequency distribution function f(v), has been used to describe the best wind distribution. Moreover, statistical estimation of wind energy potential at different altitudes (10m, 50m, 100m, 150m, and 200m) throughout vertical extrapolation and wind direction study is performed to identify the suitable high wind turbine hub. Finally, the evaluation of the predicted electrical energy produced is done while considering the judicious choice of the wind turbines and the charge factor. The Weibull parameters, frequency distribution, wind speed stability, and potentially provided by this study were motivating results for implementing wind farm in the mountain of Serra da Nogueira.