Autonomous optimal Power Flow via Convex Solution - Sequential Linear Programming

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Ilunga, Gad Monga
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Abstract
Optimal Power Flow (OPF) is at the heart of power systems operation as it is the tool of choice in solving several power systems problems at different time scales. Modernization of the OPF is one of the ways to respond to the need for modernization of the electricity grid. A modern OPF solver can also be used to reduce operational uncertainty introduced by distributed generation based on renewable energy; indeed, uncertainty from the day-ahead market can be reduced by running an intra-day market. Current OPF modeling approaches present some shortcomings: a) different devices with different mathematical models go through different adaptation processes before being introduced in the OPF and the adaptation processes introduce some errors, b) there is a lack of a systematic way to form the OPF and c) the positive modeling representation of three-phase power systems introduces some approximation thus some error. Several modeling approaches are being used to solve OPF however linearizationbased and convexification-based methods draw a lot of attention. Linearization methods have been used for years and are at the basis of many power systems problems. Linearization works well and scales well, however it is often dependent on the initial operating point, and one must ensure to stay within the valid linearization region. Convexification methods work well and often return a globally optimal solution; however, these solutions are not always applicable to the physical power system. x This dissertation develops high-fidelity physically-based object-oriented models (of grid-connected devices) and advanced computational tools and optimization models for the optimal and reliable operation of power systems. Object-oriented modeling based on first principle, driven by physics and that explicitly model the neutral is the foundation of this OPF program. This modeling approach is a multidomain modeling approach and is a step toward seamless integration of power systems applications and more specifically towards autonomous real-time control of the grid. A computer program is developed to autonomously form the OPF by operating solely on mathematical objects. The program then solves the OPF using a two-step Convex Solution-Sequential Linear Programming (CS-SLP) method that combines the strength of convexification and linearization methods to obtain a solution that is both optimal and feasible. In the first step, a generalized convexification method is used to give a thrust toward the optimal solution. Step two restores the original OPF problem and starts from the convex solution to obtain a solution that is both optimal and feasible. The performance of the advanced computational tools and optimization models is tested using three network models and two cases (generation cost optimization and optimal reactive power dispatch). The tools and models are also compared to other available global OPF solution methodologies. The results demonstrate that the solution methodology is promising as run times are acceptable and the solution is near global in most cases.
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Date
2022-07-06
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Dissertation
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