Second-Order Cone Programming for Data-Driven Fluid and Gas Energy Flow With a Tight Reformulation

The precise fluid and gas energy flow equations (FEFEs) are difficult to formulate due to the uncertain parameters. This paper proposes a data-driven approach to fit the FEFEs by polynomial functions through experimental data. Furthermore, a convex optimization model is set up to find the solution of the FEFEs, and a tight reformulation is proposed to exactly reformulate the proposed model as a second-order cone programming (SOCP) that can be tractably solved. Numerical results on several test systems show the effectiveness of the proposed method.