Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling

To reach the goal for limiting climate change, a large amount of renewable energy sources (RESs) needs to be installed and integrated into the existing electrical grid infrastructure. This leads to an increasing demand for flexibility that can be provided by the promising approach of the Energy Hub Gas (EHG). For intelligently controlling the different components of an EHG enhanced optimization methods are required. Due to the complexity of this optimization task a concept and its evaluation for enhancing the ability of an evolutionary algorithm (EA) to track several objectives is presented. With this concept a dynamic mapping of the objective functions to the fitness value of an EA by calculating a degree of fulfillment (DOF) in relation to the maximum and minimum possible boundary value is enabled. The evaluation of this concept shows a significant improvement of optimization results with increasing the DOF by 10.2%.