Sistema de gerenciamento de energia para comunidades energéticas residenciais: uma proposta que agrega resposta da demanda, geração distribuída e sistemas de armazenamento

• Main Author: Santos, Stéphanie Alencar Braga dos
• Publication Date: 2024
• Format: Doctoral thesis
• Language: por
• Source: Repositório Institucional da Universidade Federal do Ceará (UFC)
• Download full: http://repositorio.ufc.br/handle/riufc/80231
• topic: CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA; Community energy - Ceará; Gerenciamento de Energia; Eficiência Energética; Otimização; Resposta da Demanda; Energia elétrica - Consumo - Ceará; Community energy - Ceará; Energy Management; Energy Efficiency; Optimization; Demand Response; Electric power consumption - Ceará

This work is part of the Community Energy, scenario, in terms of a community's energy management system. We propose a Community Energy Management System (CEMS) that uses the concept of distributed generation, with a solar energy system and a battery storage system shared with the community members, as well as the possibility of individual solar generation for each home. The proposed system also uses the price-based Demand Response (DR) strategy, using the Brazilian Time of Use (ToU) tariff system, the White Tariff. The proposed system works on two levels: the community controller level and the consumer level (active and passive). The active consumers participates directly in the DR and have an Home Energy Energy System (HEMS) that schedules controllable loads. At the consumer level, the problem is formulated as a multi-objective optimization problem, aiming to minimize both the cost of the energy bill and the user's discomfort when scheduling or shifting controllable loads. At the community controller level, the goal is to minimize the expected overall cost by optimizing the use of battery power. To solve this problem, we employ the Geofind method for cases without demand restrictions and a hybrid Geofind + Particle Swarm Optimization (PSO) algorithm for cases with demand constraints. At the controller level, we use Genetic Algorithm (GA) to solve the problem of scheduling the charging/discharging profile of the battery. The methodology is simulated using the Python programming language, in different scenarios. The results demonstrate the effectiveness of the CEMS, performing the optimal scheduling of the battery charge/discharge profile, achieving daily financial savings of more than 20%, considering the capacities of the proposed resources in each proposed scenario.