Modeling and Forecasting the Performance of Power Distribution Networks Considering Operational Risks in Energy Imbalance Conditions

Document Type : Research Paper

Authors

School of Industrial Engineering, Iran University of Science and Technology (IUST), Tehran, Iran

Abstract

Objective: The stable performance of urban power distribution networks plays a fundamental role in ensuring a sustainable and reliable energy supply. However, increasing operational complexities and multiple risks necessitate efficient and adaptive management approaches.
Methods: This study employs system dynamics modeling and Failure Mode and Effects Analysis (FMEA) to model and forecast the performance of urban power distribution networks under energy imbalance conditions. The proposed framework introduces risk-related parameters, failure probability, maintenance delay rate, and risk-propagation factor, and embeds scenario-based forecasting to 2042, enabling analysis of feedbacks between operational risks and demand growth. The novelty of this study lies in developing an integrated SD–FMEA analytical framework that, unlike previous models, simultaneously captures the dynamic propagation of operational risks and their long-term feedback interactions with energy imbalance, network losses, and financial performance.
Results: Simulation results for the Yazd power distribution network indicate that optimized operational risk management can significantly reduce failure rates by the year 2042. Nevertheless, the projected increase in the number of subscribers to 1.2 million and the corresponding growth in electricity consumption pose new challenges for network stability.
Conclusion: While reducing operational risks improves service quality and system resilience, effective demand-side management is also essential to prevent potential network instability. Ultimately, the findings suggest that combining proactive risk management strategies with energy demand control is a key approach to maintaining the long-term sustainability of power distribution networks under energy imbalance conditions.

Keywords

References

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Journal of Optimization and Supply Chain Management
Volume 2, Issue 3
September 2025
Pages 137-160

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