Synergistic Optimization Model of Energy Storage and BF Gas Power Generation for Steel Enterprises Considering Power Spot Price and Maximum Demand

Abstract

Aiming at the cost fluctuation and demand-side management pressure faced by iron and steel enterprises under the background of power market reform, this paper puts forward a collaborative optimization model of energy storage and blast furnace surplus gas power generation, which integrates the power spot price signal and the maximum demand (MD) constraint. This model employs a multi-timescale mixed-integer nonlinear programming (MINLP) framework, integrating rolling optimization strategies and robust optimization methods. It prioritizes minimizing the enterprise's total electricity costs while simultaneously accounting for grid power purchase costs, MD penalty costs, coal-gas power generation operational costs, and energy storage operational costs. Through case analysis, it is verified that the model has obvious advantages in balancing the penalty of power purchase cost and demand and realizing global optimization. The results show that, compared with the traditional operation mode and the scenario of only considering the time-of-use price optimization, the collaborative optimization model can effectively reduce the total power consumption cost of enterprises and improve the economy and power grid security. In addition, the robustness test of the model further proves its effectiveness in dealing with the fluctuation of electricity price. This study provides a feasible solution for iron and steel enterprises in the process of energy structure transformation and energy efficiency improvement.