Structured nonconvex optimization of large-scale energy systems using PIPS-NLP

We present PIPS-NLP, a software library for the solution of large-scale structured nonconvex optimization problems on high-performance computers. We discuss the features of the implementation in the context of electrical power and gas network systems. We illustrate how different model structures arise in these domains and how these can be exploited to achieve high computational efficiency. Using computational studies from security-constrained ACOPF and line-pack dispatch in natural gas networks, we demonstrate robustness and scalability.