Optimized unit commitment for peak load management with solar PV and storage under load uncertainty - Smriti Jain; Neeraj Kanwar (Scientific Reports)
Abstract The installation of solar photovoltaics (PV) has gained momentum due to growing concerns about global warming and the UN’s SDGs addressing environmental challenges. The primary objective of this paper is to evaluate and address the impacts of load uncertainty on Unit Commitment through the implementation of storage-based PV generation, wherein PV generation and energy storage operate in the proposed coordinated manner. To deal with uncertainty, a hybrid optimization technique is utilized, which combines stochastic and robust computations. Stochastic load uncertainty scenarios are generated via probabilistic Gaussian Probability Density function (PDF) approach that reside within the defined uncertainty set, as established by the robust optimization framework. The mean scenario of load uncertainty is applied to evaluate the day-ahead UC costs. The IEEE 39-bus, ten-generator system serves as the basis for this analysis. UC is optimized via Dynamic Programming (DP) in the presence of load uncertainty levels of up to 10% across three distinct case studies. Case 1 functions as the baseline for comparison as it does not include PV-storage or load uncertainty modeling. In Case 2, the influence of load uncertainty on day-ahead UC is examined for a network that excludes PV-storage. In Case 3, the system integrates the proposed coordination based PV-storage and solves UC while managing peak demand amid increasing levels of load uncertainty—specifically at 5%, 8%, and 10%. Additionally, contingency margins are evaluated across all three cases to validate day ahead 24 hours system performance and reliability enhancements. By juxtaposing the results of UC across these three cases, this study aims to analyze the implications of gradually increasing load uncertainty, load management, and peak load regulation utilizing PV-storage systems.