Optimization of transformer loading based on hot-spot temperature using a predictive health model

In the future grid, power equipment will need to work with distributed generation, deregulation, and accelerated ageing. To this end, a model-based framework for the optimization of usage of power equipment is proposed. The framework uses a predictive health model of the equipment in order to optimize the usage of the equipment. In particular, the predictive health model predicts the hot-spot temperature of the transformers in a network over a future time window based on the expected loading. The allowed loading limits of the transformers are based on the hot-spot temperature. Therefore, the optimal loading of the transformers is maintained by performing an optimal power flow (OPF) computation of the network that takes into account hot-spot temperature dynamics. The optimization determines values for the tap position of the transformers and the active and reactive power of generators in the network. Moreover, shedding of the loads in the network is considered when the aforementioned options are not sufficient to control the loading of the transformers. A case study using the IEEE 14-bus benchmark system is presented. The shedding of the loads is minimized by using this technique.