Electric arc furnaces are used extensively in the steel industry for steel production. Development of energy savings strategies for the highly energy-intensive batch process is extremely challenging due to the complexity of the process and lack of measurements due to the harsh operating conditions. Here we introduce a new energy management approach that effectively curtails the energy cost in real-time through the implementation of economically optimal operating decisions. An economics- oriented shrinking horizon nonlinear model predictive control (NMPC) algorithm that exploits time-varying electricity prices is coupled with a multi-rate moving horizon estimator (MHE) to form an integrated decision- making framework. With a detailed first-principles dynamic model functioning at the core, the multi-variable interactions and plant variations are successfully incorporated into the control strategy to achieve reliable performance. We also present a novel initialization scheme for obtaining fast on-line solutions of the economic NMPC and multi-rate MHE dynamic optimization problems. Using this initialization algorithm, we show that the optimal input decisions are obtained with sufficient computational speed for real-time implementation. The energy usage optimization results indicate a significant reduction in the operating cost and peak electricity demand compared to the case where the electricity price profile is not updated. (C) 2018 Elsevier Ltd. All rights reserved.