Browsing by Author "Gomes, Antonio"
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- Charge/Discharge Scheduling of Electric Vehicles and Battery Energy Storage in Smart Building: a Mix Binary Linear Programming modelPublication . Foroozandeh, Zahra; Ramos, Sérgio Filipe Carvalho; Soares, João; Vale, Zita; Gomes, AntonioNowadays, the buildings have an important role in the high demand for electrical energy. Therefore, the energy management of the buildings may have significant influence on reducing the electricity consumption. Moreover, Electric Vehicles (EVs) have been considering as a power storage device in Smart Buildings (SBs) aiming to reduce the cost and consuming energy. Here, an energy management framework is proposed in which by considering the flexibility of the contracted power of each apartment, an optimal charging-discharging scheduled for EVs and Battery Energy Storage System (BESS) is defined over a long time period to minimize the electricity cost of the building. The proposed model is designed by a Mixed Binary Linear Programming formulation (MBLP) that the charging and discharging of EVs as well as BESS in each period is treated as binary decision variables. In order to validate the model, a case study involving three scenarios are considered. The obtained results indicate a 15% reduction in total electricity consumption cost and consumption energy by the grid over one year. Finally, the impact of capacity and charge/discharge rate of BESS on the power cost is analyzed and the optimal size of the BESS for assumed SB in the case study is also reported.
- A Short Review on Data Mining Techniques for Electricity Customers CharacterizationPublication . Cembranel, Samuel S.; Lezama, Fernando; Soares, João; Filipe Ramos, Sérgio; Gomes, Antonio; Vale, ZitaAn important tool to manage electrical systems is the knowledge of customers' consumption patterns. Data Mining (DM) emerges as an important tool for extracting information about energy consumption in databases and identifying consumption patterns. This paper presents a short review on DM, with a focus on the characterization of electricity customers supported on knowledge discovery in database (KDD) process. The study includes several steps: first, few concepts of the KDD process are presented; following, a short review of clustering algorithms is presented including partitional, hierarchical, fuzzy, evolutionary methods, and Self-Organizing Maps; finally, the main concepts and methods for load classification, based on load shape indices are presented. The main objective of this work is to present a short review of DM techniques applied to identify typical load profiles in electrical systems and new customers' classification.
- A Short Review on Smart Building Energy Resource OptimizationPublication . Joench, Rodrigo L.; Soares, João; Lezama, Fernando; Filipe Ramos, Sérgio; Gomes, Antonio; Vale, ZitaMotivated by increasingly strict policies aiming to reduce the pollutants emission that contribute to the greenhouse gases effect and seeking the sustainability of the electricity sector, buildings have great potential to contribute to these goals. For accounting more than a third of the world's energy consumption and combining the growing incentive in Distributed Generation (DG) and the electric vehicle (EV) industry, smart buildings will be a key development in the years to come. In this way, this work presents an introduction to the linear programming (LP) optimization method and review the main works related to the scope of Smart Buildings (SB) energy resources optimization modeled as LP, with the objective to analyze different approaches taken to meet their goals.