An adaptive discretization MINLP algorithm for optimal synthesis of decentralized energy supply systems

- A solution algorithm for a nonconvex nonlinear synthesis problem is proposed.
- We use an iterative interaction between approximate MIPs and decomposable NLPs.
- An iteratively adapted discretization forms the basis of the approximate problem.
- The algorithm outperforms MINLP solvers and pure linearization approaches.
- Real-world-based test instances used in the computational study are online available.

Decentralized energy supply systems (DESS) are highly integrated and complex systems designed to meet time-varying energy demands, e.g., heating, cooling, and electricity. The synthesis problem of DESS addresses combining various types of energy conversion units, choosing their sizing and operations to maximize an objective function, e.g., the net present value. In practice, investment costs and part-load performances are nonlinear. Thus, this optimization problem can be modeled as a nonconvex mixed-integer nonlinear programming (MINLP) problem. We present an adaptive discretization algorithm to solve such synthesis problems containing an iterative interaction between mixed-integer linear programs (MIPs) and nonlinear programs (NLPs). The proposed algorithm outperforms state-of-the-art MINLP solvers as well as linearization approaches with regard to solution quality and computation times on a test set obtained from real industrial data, which we made available online.