Sequential Quadratic Programming (SQP) Based Optimal Power Flow Methodologies for Electric Distribution System With High Penetration of DERs

• Published in: IEEE transactions on industry applications Vol. 60; no. 3; pp. 4810 - 4820
• Main Authors: Hasan, Md Shamim, Chowdhury, Md Mahmud-Ul-Tarik, Kamalasadan, Sukumar
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bus injection model; Computational modeling; distributed energy resources; Flow equations; interior point method; Load flow; Mathematical models; Nonlinear programming; optimal power flow (OPF); Optimization; Power flow; Power systems; Quadratic programming; Reactive power; second order conic programming; sequential quadratic programming; Voltage; Voltage control
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2024.3371428

This article proposes a centralized and distributed model based on nonlinear programming (NLP) for optimal power flow (OPF) analysis in power distribution networks, solved with the Sequential Quadratic Programming (SQP) algorithm. The paper proposes the method and illustrates the necessary conditions for the global optimality of the solution. The main advantages of the methods are obtaining global optimal solutions in less than a minute (for more than 2000 nodes with high penetration of DERs) without approximations and relaxations in power flow equations and improving scalability by reducing the number of iterations significantly for both centralized and distributed OPF. The OPF analysis models have been simulated in several standard distribution networks with a wide range of DER penetration. The solution from the proposed model is compared with an interior point method (IPM) based NLP algorithm (NLP-IPM) and second-order conic programming (SOCP) algorithm-based convex OPF analysis method. The analysis confirms the global optimality of the proposed algorithm and demonstrates superior convergence and accuracy compared to other OPF methods.