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- 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.
- Optimization methods for energy management in a microgrid system considering wind uncertainty dataPublication . Amoura, Yahia; Pereira, Ana I.; Lima, JoséEnergy management in the microgrid system is generally formulated as an optimization problem. This paper focuses on the design of a distributed energy management system for the optimal operation of the microgrid using linear and nonlinear optimization methods. Energy management is defined as an optimal scheduling power flow problem. Furthermore, a technical-economic and environmental study is adopted to illustrate the impact of energy exchange between the microgrid and the main grid by applying two management scenarios. Nevertheless, the fluctuating effect of renewable resources especially wind, makes optimal scheduling difficult. To increase the results reliability of the energy management system, a wind forecasting model based on the artificial intelligence of neural networks is proposed. The simulation results showed the reliability of the forecasting model as well as the comparison between the accuracy of optimization methods to choose the most appropriate algorithm that ensures optimal scheduling of the microgrid generators in the two proposed energy management scenarios allowing to prove the interest of the bi-directionality between the microgrid and the main grid.
- A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system modelsPublication . Amoura, Yahia; Pereira, Ana I.; Lima, JoséFuture power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.
- Analyzing the mathE platform through clustering algorithmsPublication . Azevedo, Beatriz Flamia; Amoura, Yahia; Rocha, Ana Maria A.C.; Fernandes, Florbela P.; Pacheco, Maria F.; Pereira, Ana I.University lecturers have been encouraged to adopt innovative methodologies and teaching tools in order to implement an interactive and appealing educational environment. The MathE platform was created with the main goal of providing students and teachers with a new perspective on mathematical teaching and learning in a dynamic and appealing way, relying on digital interactive technologies that enable customized study. The MathE platform has been online since 2019, having since been used by many students and professors around the world. However, the necessity for some improvements on the platform has been identified, in order to make it more interactive and able to meet the needs of students in a customized way. Based on previous studies, it is known that one of the urgent needs is the reorganization of the available resources into more than two levels (basic and advanced), as it currently is. Thus, this paper investigates, through the application of two clustering methodologies, the optimal number of levels of difficulty to reorganize the resources in the MathE platform. Hierarchical Clustering and three Bio-inspired Automatic Clustering Algorithms were applied to the database, which is composed of questions answered by the students on the platform. The results of both methodologies point out six as the optimal number of levels of difficulty to group the resources offered by the platform.
- Collaborative learning platform using learning optimized algorithmsPublication . Azevedo, Beatriz Flamia; Amoura, Yahia; Kantayeva, Gauhar; Pacheco, Maria F.; Pereira, Ana I.; Fernandes, Florbela P.Aware that the lack of mathematical knowledge and skills is a major problem for the development of a modern, inclusive and informed society, the MathE partnership has developed a tool that is aimed at bridging the gap that moves students away from courses that rely on a mathematical core. The MathE collaborative learning platform offers higher education students a package of scientific and pedagogical resources that allow them to be active agents in their learning pathway, by self-managing their study. The MathE platform is currently being used by a significant number of users, from all over the world, as a tool to support and engage students, ensuring new and creative ways to encourage them to improve their mathematical skills and therefore increasing their confidence and capacities. In order to enhance this platform, a visual representation of the performance of the students is already implemented, based on the recorded performance historic data for each student. This paper contains a literature review about the implementation of data mining techniques in education, followed by a description of the features of the MathE learning system and suggestions of data parameters to support the improvement of the students’ performance. Future work includes the application of optimization and learning algorithms so that the MathE platform will have a dynamical structure and act as a virtual tutor for the users. © 2021, Springer Nature Switzerland AG.
- 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
- 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.
- An improved GA-based approach for reduced non-discriminatory renewable energy curtailmentPublication . Pedroso, André Felipe Pereira; Zanatta, Giuseppe; Ferreira, Ângela P.; Pereira, Ana I.; Amoura, Yahia; Lopes, Rui Pedro; Angelos, Eduardo Werley S. dos; Vasconcelos, Fillipe Matos de; Lemos, Manuel; Pino, GabrielThe active management of the networks with high penetration of renewable energy sources faces several challenges. To maximize the utilization of low carbon electricity, the curtailment of generator units using renewables should be limited and fair among units. In this work, an attempt has been made to solve the optimal power flow using an improved Genetic Algorithm. The optimal flow formulation is oriented to the efficiency of the network, under the boundary restrictions of limited curtailment of the output of distributed generators based on renewables and non-discriminatory behaviour. The results obtained from a IEEE 14-bus test system have demonstrated the feasibility of this approach.
- 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.
- Optimal energy management of a microgrid systemPublication . Amoura, Yahia; Pereira, Ana I.; Lima, José; Boukli-Hacene, FouadA smart management strategy for the energy ows circulating in microgrids is necessary to economically manage local production and consumption while maintaining the balance between supply and demand. Finding the optimum set-points of the various generators and the best scheduling of the microgrid generators can lead to moderate and judicious use of the powers available in the microgrid. This thesis aims to apply an energy management system based on optimization algorithms to ensure the optimal control of microgrids by taking as main purpose the minimization of the energy costs and reduction of the gas emissions rate responsible for greenhouse gases. Two approaches have been proposed to nd the optimal operating setpoints. The rst one is based on a uni-objective optimization approach in which several energy management systems are implemented for three case studies. This rst approach treats the optimization problem in a uni-objective way where the two functions price and gas emission are treated separately through optimization algorithms. In this approach the used methods are simplex method, particle swarm optimization, genetic algorithm and a hybrid method (LPPSO). The second situation is based on a multiobjective optimization approach that deals with the optimization of the two functions: cost and gas emission simultaneously, the optimization algorithm used for this purpose is Pareto-search. The resulting Pareto optimal points represent di erent scheduling scenarios of the microgrid system.