Nonlinear optimization model for load profile estimation in electrical distribution systems
DOI:
https://doi.org/10.62943/rig.v3n2.2024.93Keywords:
Behavior, distribution, estimation, optimization, profile, validationAbstract
This article presents a novel methodology to address the problem of load profile estimation in electrical distribution systems, tackling challenges such as data scarcity and variability in consumer behavior. It relies on historical data provided by the Electric Company of Riobamba (EERSA), enabling the development of nonlinear optimization models that differentiate load profiles by customer type. These models focus on active and reactive power profiles, considering constraints affecting their behavior. A key aspect is validating the accuracy of these profiles, achieved by comparing data measured by medium-voltage equipment with data estimated from load profiles in a specific feeder within EERSA's distribution network. This approach not only handles data limitations and consumer behavior variability but also demonstrates the feasibility of using optimization models to enhance load profile estimation accuracy and its application in distribution system management. The results signify substantial progress in load profile estimation accuracy, offering valuable insights for planning and operating electrical distribution systems. By leveraging historical data and optimization techniques, this study contributes to refining load profile estimation methodologies, thus addressing pertinent challenges in the field and providing actionable insights for system management and planning purposes.
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