Empirical Investigation of Non-Convexities in Optimal Power Flow Problems
Optimal power flow (OPF) is a central problem in the operation of electric power systems. An OPF problem optimizes a specified objective function subject to constraints imposed by both the non-linear power flow equations and engineering limits. These constraints can yield non-convex feasible spaces that result in significant computational challenges. Despite these non-convexities, local solution algorithms actually find the global optima of some practical OPF problems. This suggests that OPF problems have a range of difficulty: some problems appear to have convex or 'nearly convex' feasible spaces in terms of the voltage magnitudes and power injections, while other problems can exhibit significant non-convexities. Understanding this range of problem difficulty is helpful for creating new test cases for algorithmic benchmarking purposes. Leveraging recently developed computational tools for exploring OPF feasible spaces, this paper first describes an empirical study that aims to characterize non-convexities for small OPF problems. This paper then proposes and analyzes several medium-size test cases that challenge a variety of solution algorithms.
M. R. Narimani et al., "Empirical Investigation of Non-Convexities in Optimal Power Flow Problems," Proceedings of the 2018 American Control Conference (2018, Milwaukee, WI), pp. 3847-3854, Institute of Electrical and Electronics Engineers (IEEE), Jun 2018.
The definitive version is available at https://doi.org/10.23919/ACC.2018.8431760
2018 American Control Conference, ACC 2018 (2018: Jun. 27-29, Milwaukee, WI)
Electrical and Computer Engineering
United States. Department of Energy
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
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