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Efficiently Including Time and Voltage Variability of Loads in Optimization of Distribution Systems Topology
Ist Teil von
IEEE transactions on industry applications, 2024-09, Vol.60 (5), p.7470-7480
Ort / Verlag
New York: IEEE
Erscheinungsjahr
2024
Quelle
IEEE Xplore
Beschreibungen/Notizen
The reconfiguration of distribution networks results in modifications to the voltage magnitudes of buses, subsequently influencing the power demands of connected loads. This variable load demand plays a pivotal role in determining the optimal system topology. While a few studies have recognized this crucial aspect, they often present non-linear models or simplified formulations. Non-linear models, though solvable with commercial solvers, demand extensive computational time, particularly when accommodating the time variability of loads in the formulation. Alternatively, solving these models may require meta-heuristic methods, introducing the challenge of obtaining precise solutions. In contrast, existing linear models have been subject to approximation through piecewise linearization, introducing a level of uncertainty. Achieving accurate solutions for reconfiguration problems within a short computational timeframe is imperative for practical operational applications. As a result, present study addresses this challenge by introducing four effective reconfiguration methods that explicitly consider the voltage dependence of time-varying loads. These techniques leverage linear solvers, enabling the computation of precise solutions within a condensed computational timeframe. By adopting these proposed methods, the study aims to enhance the efficiency and accuracy of solving distribution network reconfiguration problems, thereby facilitating practical applications in real-time operational scenarios. The linear mathematical models introduced in the current study can find exact solutions in a short computational time, unlike meta-heuristic algorithms and non-linear formulations. Additionally, the proposed time and voltage variability formulation causes the presented reconfiguration frameworks to solve the problem in very low computational time compared to models with exact load variations inclusion.