Improving the performance of non-linear multi-objective optimization problems

In optimization, the added accuracy of using non-linear relationships often comes with the trade-off of longer run times. A software solution was developed that allows modelers to easily import a seed into an RTC-Tools optimization run. Combined with time-dependent linearization, performance gains of 60% in run times were observed in complex, non-linear multi-objective operational systems. With improved run times, non-linear optimization problems become more accessible. Clients can solve more accurate models of their systems, enabling them to make more informed decisions about their energy use. Additionally, the model's advice is far more valuable when run times are fast, allowing for quicker implementation before conditions change.