Title: Large-scale MILP and MINLP problems in power system planning
Speaker: Dr. Ioannis Konstantelos
Affiliation: Department of Electrical and Electronic Engineering – Imperial College London
Location: Room 218 Huxley Building
Time: 4:00pm
Abstract. Achieving the ambitious decarbonization goals set by governments worldwide will entail significant changes to the way electrical energy is generated, transmitted and used. The cost-effective integration of inflexible low-carbon plant within conventional energy systems constitutes a significant challenge. Furthermore, transmission planners are unable to make fully-informed decisions due to the increasing uncertainty that surrounds future system developments. Recently, it has been shown that stochastic system planning based on scenario trees enables the identification of strategic opportunities for the management of long-term uncertainty. However, the description of these problems is given by large MILP models which contain thousands of binary variables and tens of millions of continuous variables and constraints. After reviewing the general characteristics of the stochastic multi-stage transmission planning problem we will present two novel solution algorithms; one based on hierarchical decomposition and one on temporal problem splitting. We will demonstrate their computational benefits and highlight how efficient solutions can inform the real-world planning process. We will also present a typology of MILP and MINLP problems encountered in energy system planning and operation.
About the speaker. Ioannis Konstantelos is a Research Associate in the Control and Power group, Electrical Engineering, Imperial College London. He obtained a PhD from Imperial College in 2013. His work has focused on the development of optimisation models for transmission and distribution system planning and operation aimed at valuing the benefit of new technologies, demonstrating the strategic value of storage and demand-side response and other flexible technologies when facing long-term uncertainty. His research interests include the application of decomposition and machine learning techniques to large-scale optimization problems for energy systems.