Financial risk analysis considering uncertainties for allocation of distributed generation in a Smart Grids environment

• Grant number: 14/21522-8
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: January 20, 2015
• End date: January 19, 2016
• Field of knowledge: Engineering - Electrical Engineering - Power Systems
• Principal Investigator: José Roberto Sanches Mantovani
• Grantee: Augusto César Rueda Medina
• Supervisor: Yong Fu
• Host Institution: Faculdade de Engenharia (FEIS). Universidade Estadual Paulista (UNESP). Campus de Ilha Solteira. Ilha Solteira , SP, Brazil
• Institution abroad: Mississippi State University (MSU), United States
• Associated to the scholarship: 12/15210-8 - Active Power Reserve for Frequency Control and Reactive Power Support Provided by Distributed Generators, BP.PD

Abstract

The benefits that distributed generation can bring along to the electrical systems suggest that it could provide not only energy to satisfy loads and system losses, but also other essential services, necessary to ensure the stability and security of the system, known as ancillary services. However, to consider the distributed generation as a reliable alternative, the uncertainties of distributed generators based on renewable energy sources with high degree of variability should be reduced by developing and applying accurate and efficient forecasting models. Moreover, operating problems may occur if distributed generators are not allocated properly, and investors must balance risk and reward through a financial risk analysis.In this research project, mathematical models considering distributed generation and optimization techniques will be addressed in a smart grids environment. Renewable resources models (wind and hydrological models) considering climate variables, a market model for energy to satisfy loads and system losses, a co-optimization market structure for reserves for frequency control supplied by distributed generation, and a financial risk analysis methodology to provide signals for investment in new distributed generation capacity, will be developed and enhanced. Optimization solvers and meta-heuristics will be used for the solution of the proposed problem. (AU)