Quantificação do risco na seleção e operação de recursos energéticos distribuídos inseridos em uma microrrede.

• Autor(a) principal: Budel, Rodrigo Alexssandre
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNIOESTE
• Texto Completo: http://tede.unioeste.br/handle/tede/5329
• topic: Microrredes; Recurso Energético Distribuído; Incerteza; Risco; Var; CVaR; Microgrid; Distributed energy resourc; Uncertainty; Risk; VaR; CVaR; ENGENHARIAS::ENGENHARIA ELETRICA

Microgrids can be seen as a small controllable electrical power system, which locally integrate charges with distributed energy resources (REDs). However, implementing, expanding and operating a microgrid brings with it several economic, technical and operational challenges that must be faced, among them the quantification of the investment risk in these REDs. Thus, this work presents an optimization model for the selection and operation of REDs inserted in a microgrid, whose objective is to minimize the risk to which the decision-maker is exposed in the face of uncertainty in demand and in wind and solar energy generation. The uncertainties in these parameters have been treated through a decision tree and the risk evaluation is performed using the Value at Risk (VaR) and the Conditional Value at Risk (CVaR), the latter risk metric being the main contribution of the work. The resulting mathematical formulation constitutes a mixed integer linear programming model that was implemented in GAMS language and solved with CPLEX solver. The results obtained with the model have made it possible to determine the economic benefits that could be gained from investing in REDs in a microgrid as well as highlight the impact that can produce the intermittent nature of renewable resources and the uncertainty in demand about the variability of these benefits. The results also show the risk measures cited, serving as a tool to assist in making decisions regarding the implementation of REDs and optimized energy management in a microgrid.