%0 Conference Paper
%B 31st European Conference on Operational Research.
%D 2021
%T Multistage Scenario Trees Generation for Electricity Markets Optimization
%A Guevara, Marlyn Dayana Cuadrado
%A Heredia, F.-Javier
%C Athens
%K research; multistage stochastich programming; virtual power plants; electricity markets; scenarios tree generation
%U https://euro2021athens.com/
%X The presence of renewables in electricity markets optimization have generated a high level of uncertainty in the data, which has led to a need for applying stochastic optimization to model this kind of problems. In this work, we apply Multistage Stochastic Programming (MSP) using scenario trees to represent energy prices and wind power generation. We developed a methodology of two phases where, in the first phase, a procedure to predict the next day for each random parameter of the MSP models is used, and, in the second phase, a set of scenario trees are built through Forward Tree Construction Algorithm (FTCA) and a modified Dynamic Tree Generation with a Flexible Bushiness Algorithm (DTGFBA). This methodology was used to generate scenario trees for the Multistage Stochastic Wind Battery Virtual Power Plant model (MSWBVPP model), which were based on MIBEL prices and wind power generation of a real wind farm in Spain. In addition, we solved three dierent case studies corresponding to three dierent hypotheses on the virtual power plant?s participation in electricity markets. Finally, we study the relative performance of the FTCA and DTGFBA scenario trees, analysing the value of the stochastic solution through the Forecasted Value of the Stochastic Solution (FVSS) and the classical VSS for the 366 daily instances of the MSWBVPP problem spanning a complete year.
%8 11-14/07/2021
%9 Invited presentation
%@ ISBN 978-618-85079-1-3