A novel stochastic-programming-based energy management system to promote self-consumption in industrial processes

Abstract

The introduction of non-conventional energy sources (NCES) to industrial processes is a viable alternative to reducing the energy consumed from the grid. However, a robust coordination of the local energy resources with the power imported from the distribution grid is still an open issue, especially in countries that do not allow selling energy surpluses to the main grid. In this paper, we propose a stochastic-programming-based energy management system (EMS) focused on self-consumption that provides robustness to both sudden NCES or load variations, while preventing power injection to the main grid. The approach is based on a finite number of scenarios that combines a deterministic structure based on spectral analysis and a stochastic model that represents variability. The parameters to generate these scenarios are updated when new information arrives. We tested the proposed approach with data from a copper extraction mining process. It was compared to a traditional EMS with perfect prediction, i.e., a best case scenario. Test results show that the proposed EMS is comparable to the EMS with perfect prediction in terms of energy imported from the grid (slightly higher), but with less power changes in the distribution side and enhanced dynamic response to transients of wind power and load. This improvement is achieved with a non-significant computational time overload.