Percorrer por autor "Mota, Bruno"
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- Demand Response Driven by Distribution Network Voltage Limit Violation: A Genetic Algorithm Approach for Load ShiftingPublication . Canizes, Bruno; Mota, Bruno; Ribeiro, Pedro; Vale, ZitaThe residential sector electricity demand has been increasing over the years, leading to an increasing effort of the power network components, namely during the peak demand periods. This demand increasing together with the increasing levels of renewable-based energy generation and the need to ensure the electricity service quality, namely in terms of the voltage profile, is challenging the distribution network operation. Demand response can play an important role in facing these challenges, bringing several benefits, both for the network operation and for the consumer (e.g., increase network components lifetime and consumers bill reduction). The present research work proposes a genetic algorithm-based model to use the consumers’ load flexibility with demand response event participation. The proposed method optimally shifts residential loads to enable the consumers’ participation in demand response while respecting consumers’ preferences and constraints. A realistic low voltage distribution network with 236 buses is used to illustrate the application of the proposed model. The results show considerable energy cost savings for consumers and an improvement in voltage profile.
- Production Line Optimization to Minimize Energy Cost and Participate in Demand Response EventsPublication . Mota, Bruno; Gomes, Luis; Faria, Pedro; Ramos, Carlos; Vale, Zita; Correia, ReginaThe scheduling of tasks in a production line is a complex problem that needs to take into account several constraints, such as product deadlines and machine limitations. With innovative focus, the main constraint that will be addressed in this paper, and that usually is not considered, is the energy consumption cost in the production line. For that, an approach based on genetic algorithms is proposed and implemented. The use of local energy generation, especially from renewable sources, and the possibility of having multiple energy providers allow the user to manage its consumption according to energy prices and energy availability. The proposed solution takes into account the energy availability of renewable sources and energy prices to optimize the scheduling of a production line using a genetic algorithm with multiple constraints. The proposed algorithm also enables a production line to participate in demand response events by shifting its production, by using the flexibility of production lines. A case study using real production data that represents a textile industry is presented, where the tasks for six days are scheduled. During the week, a demand response event is launched, and the proposed algorithm shifts the consumption by changing task orders and machine usage.
- Residential load shifting in demand response events for bill reduction using a genetic algorithmPublication . Mota, Bruno; Faria, Pedro; Vale, ZitaFlexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual.
- Residential load shifting in demand response events for bill reduction using a genetic algorithmPublication . Mota, Bruno; Faria, Pedro; Vale, ZitaFlexible demand management for residential load scheduling, which considers constraints, such as load operating time window and order between them, is a key aspect in demand response. This paper aims to address constraints imposed on the operation schedule of appliances while also participating in demand response events. An innovative crossover method of genetic algorithms is proposed, implemented, and validated. The proposed solution considers distributed generation, dynamic pricing, and load shifting to minimize energy costs, reducing the electricity bill. A case study using real household workload data is presented, where four appliances are scheduled for five days, and three different scenarios are explored. The implemented genetic algorithm achieved up to 15% in bill reduction, in different scenarios, when compared to business as usual.
- Scheduling of a textile production line integrating PV generation using a genetic algorithmPublication . Ramos, Carlos; Barreto, Rúben; Mota, Bruno; Gomes, Luis; Faria, Pedro; Vale, ZitaConsidering the technological advances of the industrial sector today, it appears that the management of energy resources has become increasingly prominent. Thus, to make this management more efficient, it is necessary to take into account the production planning and scheduling concept, since it allows influencing the scheduling of production at the level of cost and efficiency. Thus, the objective of this paper is to present a methodology that allows making the best possible scheduling of a textile production line to optimize it. This optimization is elaborated with the help of genetic algorithms, and, as it can be verified in this paper, it is possible to make an optimization of the production line at the level of energy cost or the level of energy consumption or optimization of both. Thus, the case study of this paper is based on a textile production line that produces a variety of products through three machines capable of performing numerous tasks, which can be done on more than one machine. Likewise, this production line enjoys photovoltaic production. This paper presents several case studies that allow for highlighting the impact of the methodology covered in the respective production line, where it is illustrated through different graphics.