The impact of flexibility on demand-side management and the need for consumer-oriented demand response

• Ano de defesa: 2023
• Autor(a) principal: ALMEIDA, Vitor Augusto Correa Cortez
• Orientador(a): RABÊLO, Ricardo de Andrade Lira
• Banca de defesa: RABÊLO, Ricardo de Andrade Lira , CARVALHO, Arthur , SANTOS, Silvio Giuseppe Di , PINHEIRO, Plácido Rogerio , LEÃO, Erico Meneses
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO DOUTORADO EM CIÊNCIA DA COMPUTAÇÃO
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: redes elétricas inteligentes; gerenciamento pelo lado da demanda; resposta da demanda; sistemas de gerenciamento de energia residencial; flexibilidade do consumidor; otimização multiobjetivo.
• Palavras-chave em Inglês: smart grids; demand­side management; demand response; home energy management systems; consumer flexibility; multi­objective optimization.
• Área do conhecimento CNPq: Ciência da Computação
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/5019

In the context of smart electricity grids, demand­side management is a set of measures to motivate end consumers into adapting their energy demand to the available gen- eration resources. To achieve this goal, demand­side management solutions reward flexible consumers through demand response programs. Recent trends point towards the development of home energy management systems to leverage the potential of residential consumers to contribute to demand response efforts. However, current re- search is still ongoing and more studies are needed on how to design demand response programs with home energy management systems that appeal to end consumers. This study addresses limitations of home energy management systems in the literature and proposes a consumer­centric demand­side management approach based on a bi­level optimization model. First, consumers solve a lower­level multi­objective load scheduling problem to optimize their individual savings and comfort. Then, demand aggregators solve an upper­level single­objective demand profile aggregation problem to optimize its peak­to­average ratio. Experiments investigate the impact of optimization methods, distributed energy resources, consumer preferences and behavioral patterns on the demand response program’s operation. Results indicate that while distributed energy resources contribute to reduce consumption costs and discomfort, they incur a negative effect on the community demand’s peak­to­average ratio. In addition, consumer flexibil- ity, dynamic pricing, and active consumer participation in the demand reponse process are key factors to the success of demand­side management solutions. Future work can improve in aspects such as modeling uncertainty, sustainability, and grid stability, as well as experimenting with real consumer communities.