joint work with Claudia Sagastizábal
We consider risk-averse stochastic programming models for the Generation and Expansion Planning problem for energy systems with here-and-now investment decisions and generation variables of recourse. The resulting problem is coupled both along scenarios and along power plants. To achieve decomposition, we combine the Progressive Hedging algorithm to deal with scenario separability, and an inexact proximal bundle method to handle separability for different power plants in each scenario subproblem. By suitably combining these approaches, a solution to the original problem is obtained.
joint work with Benjamin Leibowicz, Erhan Kutanoglu
We develop a large-scale linear programming framework that optimizes long-term capacity investment decisions and operational schedules for energy supply and end-use technologies. Our methodology extends an energy-economic modeling framework that simultaneously optimizes generation investments, the locations of these investments as well as the underlying transmission network linking generation facilities to different demand regions. We study the evolution of technology mix and transmission infrastructure for the US electricity market over the span of 2016-2050.
joint work with Stefan Ulbrich
The optimal design of energy networks is a decisive prerequisite for the realization of future decentralized supply structures including renewable energy. The different energy carriers must be planned simultaneously with regard to generation and load conditions. Our aim is to minimize the costs of the various acquisition opportunities, while satisfying the heat and power demands. The design decisions and the detailed description of the flow dynamics within each network result in a mixed integer nonconvex optimization problem. Furthermore numerical results for different instances are presented.