Zhang, L.; Fakhrabadi, R.; Khoshkhoo, M.; Salama, H. A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem. Journal of Energy and Power Technology 2022, 04, 1–15, doi:10.21926/jept.2204031.
Zhang, L.; Fakhrabadi, R.; Khoshkhoo, M.; Salama, H. A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem. Journal of Energy and Power Technology 2022, 04, 1–15, doi:10.21926/jept.2204031.
Zhang, L.; Fakhrabadi, R.; Khoshkhoo, M.; Salama, H. A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem. Journal of Energy and Power Technology 2022, 04, 1–15, doi:10.21926/jept.2204031.
Zhang, L.; Fakhrabadi, R.; Khoshkhoo, M.; Salama, H. A Robust Second-Order Conic Programming Model with Effective Budget of Uncertainty in the Optimal Power Flow Problem. Journal of Energy and Power Technology 2022, 04, 1–15, doi:10.21926/jept.2204031.
Abstract
Integrating large-scale wind energy in modern power systems is demanding more efficient mathematical models to properly address classical assumptions in power system problems. In particular, there are two main assumptions in power system problems with wind integration that have not been adequately studied yet; First, non-linear AC power flow equations have been linearized in most of the literature. Such simplifications can lead to inaccurate power flow calculations that may result in other technical issues. Second, wind power uncertainties are inevitable and have been mostly modelled using the traditional uncertainty modelling approaches, that may not be suitable for large-scale wind power integration. In this paper, we address both challenges: we present a tight second-order conic relaxation (SOCR) for optimal power flow (OPF) problem, and simultaneously, implement the new effective budget of uncertainty approach for uncertainty modelling that determines the maximum wind power admissibility first and then addresses the uncertainty in the model. To the best of our knowledge, this is the first study that proposes an effective robust second-order conic programming (ERSOCP) model that simultaneously addresses the issues of power flow linearization and wind power uncertainty with the new paradigm on the budget of uncertainty approach. Our numerical results show the merit of the proposed model against traditional linearized power flow equations as well as traditional uncertainty modelling approaches.
Keywords
renewable energy sources; wind uncertainty; effective budget of uncertainty; second-order conic relaxation; AC power flow equations
Subject
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
