Hour-Ahead Price Based Energy Management Scheme for Industrial Facilities

Abstract

Price-based demand response (PBDR) offers a significant opportunity for electricity consumers to dynamically balance their energy demand in response to time-varying electricity prices, and therefore ease the burden on the grid during peak times. However, despite being the primary energy consumers, there is little research carried out on implementing PBDR in industrial facilities, especially on real-time price (RTP) based DR. In this study, we propose a DR scheme based on hour-ahead RTP for industrial facilities. The scheme implements an artificial neural network based price forecasting model to forecast unknown future prices to support global time horizon optimization. Based on the forecasting price, the energy cost minimization problem is formulated by mixed integer linear programming. This paper includes a practical case study of the whole process of steel powder manufacturing for performance analysis. The results show that the proposed scheme is capable of balancing the energy demand and reducing energy costs while satisfying production targets.