@phdthesis { ,
	title = {Multistage Scenario Trees Generation for Renewable Energy Systems Optimization},
	year = {2020},
	pages = {194},
	school = {Universitat Politècnica de Catalunya},
	type = {PhD Thesis},
	address = {Barcelona},
	abstract = {The presence of renewables in energy systems optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to modelling problems with this characteristic. The method followed in this thesis is Multistage Stochastic Programming (MSP). Central to MSP is the idea of  representing uncertainty (which, in this case, is modelled with a stochastic process) using scenario trees. In this thesis, we developed a methodology that starts with available historical data; generates a set of scenarios for each random variable of the MSP model; defines individual scenarios that are used to build the initial stochastic process (as a fan or an initial scenario tree); and builds the final scenario trees that are the approximation of the stochastic process.},
	keywords = {research;  Battery energy storage systems; Electricity markets; Ancillary services market; Wind power generation; Virtual power plants; Multistage Stochastic programming;phd thesis},
	URL = {http://hdl.handle.net/2117/334943},
	author = {Marlyn Dayana Cuadrado Guevara}
}

