Heuristics for Transmission Expansion Planning in Low-Carbon Energy System Models

摘要: Governments across the world are planning to increase share of renewables in their energy systems. The siting new wind and solar power plants requires close coordination with grid planning, hence co-optimization investment generation transmission expansion spatially temporally resolved models is an indispensable but complex problem. Particularly considerations (TEP) add problem’s complexity. Even if flow equations linearized, optimization problem still bilinear mixed-integer due dependence line on impedance a discrete set options. While it possible linearize this nonlinear program (MINLP) by applying big-M disjunctive relaxation, resulting MILP hard solve using state-of-the-art solvers for large-scale system models. In paper we therefore develop heuristics incorporate integer responsive impedances models, while retaining lower computational effort continuous linear programming (LP) sequential (SLP) techniques, relaxation discretization approaches. We benchmark performance against results exact formulation policy-relevant case study German terms speed-up computation time, deviation from optimal total cost, similarity expansion. Using reduce times joint optimisation 82% maximal cost only 1.5%. closely mirror MINLP considerable time savings low-carbon