A Many-Objective Configuration Optimization for Building Energy Management

For a commercial building or campus, the management of local energy production, storage, and consumption, promises substantial gains in efficiency and reduced costs and emissions. When facility managers are planning updates to an existing building complex, they face a variety of options for investment. This work targets to provide support for this investment decision by performing a many-objective optimization (MAO) of the system configuration considering initial investment cost, running costs, CO2 emissions, and system resilience. In our specific example the potential investment covers a photo voltaic (PV) system, a stationary battery, and a heat storage. We also consider potential changes to the operation of an existing co-generator for heat and power (CHP), by optimizing controller parameters. The proposed system is simulated using a Modelica-based software environment. In this work we show the results of our configuration optimization using the well-known NSGA-III algorithm and also consider the problem of variable run-times of the simulator on the optimization process especially for a parallel execution of fitness evaluations on a computing cluster.