Cost minimization of large-scale infrastructure for electricity generation and transmission

Abstract

Electricity infrastructure confronts societies with immense costs as it must ensure the generation of power and its transmission to locations with consumption requirements. We minimize these costs by formulating an electricity generation and transmission problem that facilitates the design of electricity infrastructure on a macro level. Our problem specifies the capacity, type, and location of power plants and, at the same time, determines the appropriate arrangement of high-voltage transmission lines in order to fulfill the demand of individual cities. We specifically incorporate the non-linear nature of cost functions for power generation that are common in practice. This results in a mixed integer non-linear problem, for which the branch-and-reduce solver from GAMS exceeds runtime constraints, even for small instances with 25 locations. As a remedy, we develop heuristics based on the reduced variable neighborhood search and the greedy randomized adaptive search procedure (GRASP). Their performance enables us to address large-scale problems that arise in real-world applications. We demonstrate this with an actual, nationwide example that spans all 4,537 municipalities in Germany.

Seasonal hydrogen storage decisions under constrained electricity distribution capacity

“…The high peaks of solar energy production in summer often cause cable congestion which may inhibit the installation of solar parks. Designing electricity grids that can accommodate location-specific energy generation and take into account physical constraints is therefore a challenging task [26].…”