Browsing by Author "Fernandes, Filipe"
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- ANN-Based LMP forecasting in a distribution network with large penetration of DGPublication . Soares, Tiago; Fernandes, Filipe; Morais, H.; Faria, Pedro; Vale, ZitaIn recent years, power systems have experienced many changes in their paradigm. The introduction of new players in the management of distributed generation leads to the decentralization of control and decision-making, so that each player is able to play in the market environment. In the new context, it will be very relevant that aggregator players allow midsize, small and micro players to act in a competitive environment. In order to achieve their objectives, virtual power players and single players are required to optimize their energy resource management process. To achieve this, it is essential to have financial resources capable of providing access to appropriate decision support tools. As small players have difficulties in having access to such tools, it is necessary that these players can benefit from alternative methodologies to support their decisions. This paper presents a methodology, based on Artificial Neural Networks (ANN), and intended to support smaller players. In this case the present methodology uses a training set that is created using energy resource scheduling solutions obtained using a mixed-integer linear programming (MIP) approach as the reference optimization methodology. The trained network is used to obtain locational marginal prices in a distribution network. The main goal of the paper is to verify the accuracy of the ANN based approach. Moreover, the use of a single ANN is compared with the use of two or more ANN to forecast the locational marginal price.
- Can ZrAlN thin films be used as thermistor sensors for temperature assessment?Publication . Martins, Bruno; Patacas, Carlos; Cavaleiro, Albano; Faia, Pedro; Bondarchuk, Oleksandr; Fernandes, FilipeThe electrical characteristics and conduction mechanisms of ZrAlN thin films for their potential use as thermistor sensors were assessed. Various compositions of Zr1-xAlxN were synthesized by sputtering and studied up to 200 °C to understand their sensitivity and applicability. Among the compositions studied, the ones with x = 0.34 and x = 0.46 showed the highest sensitivities, reaching values close to 3000 K. However, the thermo-resistive properties exhibited by these compositions limited their utilization above 100 °C. Zr1-xAlxN film compositions with x higher than 0.46 showed amorphous structures and were found to be insulative. Composition with x = 0.26, within the cubic phase, showed the most promising electrical properties regarding temperature sensing in the studied range. XPS analysis of this composition confirmed the presence of Zr-N and Al-N bonds, with a Zr3+ oxidation state, which suggests the availability of a free electron contributing to the electrical conduction. Impedance measurements performed at different temperatures for this composition revealed the dominant role of the grain boundaries in the conduction mechanism, based upon electron hopping between grains, overcoming the energy barrier imposed by the grain boundaries. ZrAlN thin films demonstrate negative temperature coefficient (NTC) thermistor behavior, expanding their applications beyond protective coatings to temperature monitoring.
- Case based reasoning with expert system and swarm intelligence to determine energy reduction in buildings energy managementPublication . Faia, R.; Pinto, Tiago; Abrishambaf, Omid; Fernandes, Filipe; Vale, Zita; Corchado, Juan ManuelThis paper proposes a novel Case Based Reasoning (CBR) application for intelligent management of energy resources in residential buildings. The proposed CBR approach enables analyzing the history of previous cases of energy reduction in buildings, and using them to provide a suggestion on the ideal level of energy reduction that should be applied in the consumption of houses. The innovations of the proposed CBR model are the application of the k-Nearest Neighbors algorithm (k-NN) clustering algorithm to identify similar past cases, the adaptation of Particle Swarm Optimization (PSO) meta-heuristic optimization method to optimize the choice of the variables that characterize each case, and the development of expert systems to adapt and refine the final solution. A case study is presented, which considers a knowledge base containing a set of scenarios obtained from the consumption of a residential building. In order to provide a response for a new case, the proposed CBR application selects the most similar cases and elaborates a response, which is provided to the SCADA House Intelligent Management (SHIM) system as input data. SHIM uses this specification to determine the loads that should be reduced in order to fulfill the reduction suggested by the CBR approach. Results show that the proposed approach is capable of suggesting the most adequate levels of reduction for the considered house, without compromising the comfort of the users.
- Ceramic-reinforced HEA matrix composites exhibiting an excellent combination of mechanical propertiesPublication . Mehmood, M. Adil; Shehzad, Khurram; Mujahid, M.; Yaqub, Talha Bin; Godfrey, Andy; Fernandes, Filipe; Muhammad, F. Z.; Yaqoob, KhurramCoCrFeNi is a well-studied face centered cubic (fcc) high entropy alloy (HEA) that exhibits excellent ductility but only limited strength. The present study focusses on improving the strength-ductility balance of this HEA by addition of varying amounts of SiC using an arc melting route. Chromium present in the base HEA is found to result in decomposition of SiC during melting. Consequently, interaction of free carbon with chromium results in the in-situ formation of chromium carbide, while free silicon remains in solution in the base HEA and/or interacts with the constituent elements of the base HEA to form silicides. The changes in microstructural phases with increasing amount of SiC are found to follow the sequence: fcc → fcc + eutectic → fcc + chromium carbide platelets → fcc + chromium carbide platelets + silicides → fcc + chromium carbide platelets + silicides + graphite globules/flakes. In comparison to both conventional and high entropy alloys, the resulting composites were found to exhibit a very wide range of mechanical properties (yield strength from 277 MPa with more than 60% elongation to 2522 MPa with 6% elongation). Some of the developed high entropy composites showed an outstanding combination of mechanical properties (yield strength 1200 MPa with 37% elongation) and occupied previously unattainable regions in a yield strength versus elongation map. In addition to their significant elongation, the hardness and yield strength of the HEA composites are found to lie in the same range as those of bulk metallic glasses. It is therefore believed that development of high entropy composites can help in obtaining outstanding combinations of mechanical properties for advanced structural applications.
- Combined heat and power and consumption optimization in a SCADA-based systemPublication . Fernandes, Filipe; Morais, H.; Faria, Pedro; Vale, Zita; Ramos, CarlosThe operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.
- Comparative analysis of microstructural, compositional, and grazing incidence characteristics of oxide scale on 316L steel: SLM vs. wrought conditionsPublication . Sehat, Alireza; Hadi, Morteza; Isfahani, Taghi; Fernandes, Filipe; Fernandes, FilipeThe aim of this research is to compare the oxidation behavior and characteristics of oxide scale of 316L steel produced by two methods: selective laser melting (SLM) and conventional casting and forming (wrought). To this end, the initial composition and microstructure of samples produced by those methods were first studied. Thermogravimetric analysis (TGA) and long-term isothermal oxidation tests were carried out on the samples and the oxidation kinetics were compared. The oxidized samples were then examined by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and grazing incidence X-ray diffraction (GIXRD). The results indicated that in the temperature range of 600 °C–900 °C, the oxidation resistance of the SLM alloy is lower than that of the wrought alloy, especially at 800 °C. This is attributed to the combined effect of: i) smaller grain size due to the rapid solidification in the SLM alloy that increases the paths of oxygen penetration, ii) lower presence of chromium and manganese elements in the oxide layer and iii) preferential growth of iron oxide in the form of hillocks on the surface. Surface and cross-section analysis of the oxide layers show that iron oxide is dominant on the surface of the SLM sample at temperatures of 600 °C and 800 °C, and at 800 °C its extended hilly growth leads to significant spallation of the oxide scale and an exponential increase in the oxidation rate. However, at 900 °C, with the formation of a continuous oxide layer containing Fe2MnO4 and CrMnO4, the oxidation rate significantly decreases in both alloys.
- Comparative investigation of friction stir welds reinforced with graphene nanoplatelets and copper in AA6082-T6 alloyPublication . Biradar, Rahul; Patil, Sachinkumar; Sharma, Priyaranjan; Fernandes, Filipe; Fernandes, FilipeFriction stir welding (FSW)represents a solid-state welding method renowned for producing highquality joints, particularly in aluminum alloys. This study focuses on enhancing weld strength in the aerospace alloy AA6082-T6. The research involved conducting experiments to create FSW joints in AA6082-T6 by incorporating graphene nanoplatelets(GNPs) and copper as filler materials. Various characteristics of the joints, including microhardness, tensile strength, wear resistance, and corrosion behavior, were meticulously investigated. The experimental findings demonstrated that AA6082-T6 joints reinforced with GNPs exhibited significantly higher weld strength than conventional joints. This improvement can be attributed to the superior bonding and reinforcing effects of GNPs within the aluminum matrix. Furthermore, the GNPs incorporated joints displayed enhanced electrochemical and wear properties. This innovative approach in FSW presents a promising avenue for enhancing weld strength across diverse alloys through the integration of different reinforcement materials.
- Context analysis in energy resource management residential buildingsPublication . Madureira, Bruno; Pinto, Tiago; Fernandes, Filipe; Vale, ZitaThis paper presents a context analysis methodology to improve the management of residential energy resources by making the decision making process adaptive to different contexts. A context analysis model is proposed and described, using a clustering process to group similar situations. Several clustering quality assessment indices, which support the decisions on how many clusters should be created in each run, are also considered, namely: the Calinski Harabasz, Davies Bouldin, Gap Value and Silhouette. Results show that the application of the proposed model allows to identify different contexts by finding patterns of devices' use and also to compare different optimal k criteria. The data used in this case study represents the energy consumption of a generic home during one year (2014) and features the measurements of several devices' consumption as well as of several contextual variables. The proposed method enhances the energy resource management through adaptation to different contexts.
- Context classification in energy resource management of residential buildings using Artificial Neural NetworkPublication . Madureira, Bruno; Pinto, Tiago; Fernandes, Filipe; Vale, Zita; Ramos, CarlosThis paper proposes an Artificial Neural Network (ANN) based approach to classify different contexts, with the goal of enhancing the management of residential energy resources. The increasing penetration of renewable based generation has completely changed the paradigm of the power and energy sector. The intermittent nature of these resources requires the system to incentivize the adaptability of consumers in order to guarantee the balance between generation and consumption. This leads to the emergence of several incentives with the objective of increasing the flexibility from the consumer's side. This, allied to the increasing price of electricity, leads to an increasing need for consumers to adapt their consumption in order to improve energy efficiency, decrease energy bills, and achieve a better use of their own generation resources. With this, several House Management Systems (HMS), and Building Energy Management Systems (BEMS) have emerged. These systems allow adapting the consumption (or suggesting changes in consumers' habits) according to several factors. However, in order to make this management truly smart, there is a need for adaptation to different contexts, so that changes can be done accordingly to the different situations that are faced at each time. This paper addresses this problem by proposing a novel methodology that enables classifying different situations in different contexts, according to different contextual variables.
- Contextual and Environmental Awareness Laboratory for Energy Consumption ManagementPublication . Gomes, Luis; Fernandes, Filipe; Faria, Pedro; Silva, Marco; Vale, Zita; Ramos, CarlosThe recent changes on power systems paradigm requires the active participation of small and medium players in energy management. With an electricity price fluctuation these players must manage the consumption. Lowering costs and ensuring adequate user comfort levels. Demand response can improve the power system management and bring benefits for the small and medium players. The work presented in this paper, which is developed aiming the smart grid context, can also be used in the current power system paradigm. The proposed system is the combination of several fields of research, namely multi-agent systems and artificial neural networks. This system is physically implemented in our laboratories and it is used daily by researchers. The physical implementation gives the system an improvement in the proof of concept, distancing itself from the conventional systems. This paper presents a case study illustrating the simulation of real-time pricing in a laboratory.