Sistema para resposta da demanda de cargas residenciais baseado em nuvem

• Autor(a) principal: Del Rio, Larissa Souto
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Manancial - Repositório Digital da UFSM
• dARK ID: ark:/26339/0013000015bpn
• Texto Completo: http://repositorio.ufsm.br/handle/1/25893
• topic: Gerenciamento e controle de cargas; Otimização de consumo; Redes elétricas inteligentes; Resposta da demanda; Loads management and contro; Consumption optimization; Smart grids; Demand response; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA

The utility’s primary goal is to keep the electrical system operating normally, with as few faults as possible. This directly reflects the relationship between energy production and consumption. Demand always has to be less than supply, so that there is never a shortage of energy. Every day, more consumers connect different loads to the electricity system, with very different consumption and generation characteristics. For the energy supply to be sustained, some incentives can be created. These incentives can be characterized by demand response (DR), a set of mechanisms used to manage user consumption in relation to electricity supply. In general, the demand response seeks to reduce energy consumption at critical moments (high demand) through the delivery of incentives to consumers, such as, for example, variations in the price of the electricity tariff. From this, this dissertation presents a system composed of algorithms and secure communication architecture capable of automatically managing residential loads in consumers participating in some demand response programs. The system’s main objective is to respond to the utility’s requests, always taking into account the preferences and needs of the consumer with regard to the controllable appliances existing in the residence’s electrical network. All control is done automatically from the decisions made by the algorithms. The algorithms have user preferences as inputs, such as operating hours of each device, priorities of each device, consumption, and status of the devices. A test scenario containing seven controllable loads showed that the developed algorithms fulfill their role. It is possible to observe the reduction of the load curve at times when the tariff is higher and/or when an RD request is received from the energy utility. In terms of data security, the architecture created to implement the control proved to be quite robust, encrypting the data of the messages transmitted.