07-31, 13:20–13:30 (UTC), Green Track
Planning the operation of Power Systems is an important task to guarantee low operational costs and reliability. In practice, model simplifications are used given problem complexity. The objective of this work is to propose a framework, comprised of a methodology and the HydroPowerModels.jl Julia package for testing the operative and economic impact of modeling simplifications over the network power-flow in hydrothermal power systems.
One of the most efficient algorithms for solving hydrothermal operation planning problems, which are large-scale multi-stage stochastic models, is the so-called stochastic dual dynamic programming (SDDP) algorithm. Operation planning of power systems aims to assess the value of the scarce resources (e.g. water) to feed short-term dispatch models used in the actual implementation of the decisions. When the planning model significantly deviates from the reality of the implemented operation, decision policies are said to be time-inconsistent. Recent literature has explored different sources of inconsistency such as time-inconsistent dynamic risk measures, inaccurate representation of the information process and simplifications in the network planning model. This work addresses the time-inconsistency due to simplifications in the network representation in the planning model extending the existing literature.
The objective of this work is to propose a framework, comprised of a methodology and an open-source computational package, for testing the operative and economic impact of modeling simplifications over the network power-flow in hydrothermal power systems.
In this presentation, we will discuss how the HydroPowerModels.jl package models hydrothermal operation planning problems and how we have to use it to study the impacts of time-inconsistency in the operation of hydrothermal power systems.
Andrew received a BSc in control engineering from PUC-RIO and a BSc in general engineering from École Centrale de Marseille. He also holds a master's in Electrical Engineering with an emphasis on Operation Research, focusing on Power Systems and Energy markets. Some of his previous projects revolve around energy economic dispatch analysis and simulation, financial data classification and portfolio optimization. His main interests are optimization, decisions under uncertainty and machine learning.
Currently, Andrew is part of the Research team at Invenia Labs.