Abstarct: The steel industry is one of the most energy-intensive industries in which the electricity cost is a significant portion of production cost. The steel plants procure the required electricity through bilateral contracts and energy markets, and thus determination of an appropriate energy portfolio for an upcoming month is one of their main concerns. Purchasing energy greater than the required value leads to additional costs for the factory, while purchasing energy less than its need forces the managers to procure energy at higher prices close to the time of power delivery. In this condition, making informed decisions necessitates design and implementation of mid-term energy management in the steel plant. To ensure an efficient and practical energy portfolio, furnaces are given special attention due to their high energy consumption and long repair times. In this thesis, the mid-term energy management problem of steelmaking process of a steel plant considering the monthly scenarios of availability of furnaces and demand-response programs is modeled using the stochastic programming approach in an upper level. Once the availability of furnaces is known in each day, daily scheduling of steelmaking equipment by considering the limitations and flexibility of production process and minimizing the electricity cost is carried out in a lower level. The mid- and short-term energy management problems of steel plant are implemented as mixed-integer linear programming models. The simulation results in Shahrood Steel Plant indicate that the total energy purchased through the scenario-baxsed model has decreased by 32.38% compared to the deterministic model, leading to a 32.77% reduction in the electricity cost. Additionally, the resulting profit through the scenario-baxsed model has increased by 1.9%.