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Title	Distribution Network Expansion Planning Considering Source-Load Uncertainty Coordination Based On Stochastic Programming, Monte Carlo Simulation, And Optical Sensing
ID_Doc	20746
Authors	Nie W.; Zhou Y.; Yang M.; Dong S.
Year	2025
Published	Advances in Transdisciplinary Engineering, 70
DOI	http://dx.doi.org/10.3233/ATDE250284
Abstract	The rapid integration of renewable energy sources (RES) and distributed energy resources (DER) into modern power systems introduces significant challenges for distribution network expansion planning (DNEP). These challenges primarily arise from the inherent uncertainty in renewable generation and load demand, which traditional deterministic and robust optimization models struggle to address effectively. This study proposes a hybrid stochastic optimization framework that integrates Monte Carlo simulation with scenario reduction techniques to improve decision-making under uncertainty. The framework employs stochastic programming to minimize investment and operational costs while ensuring system reliability across diverse scenarios. Monte Carlo simulation models high-dimensional uncertainties, and scenario reduction methods enhance computational efficiency by selecting representative scenarios. By leveraging interdisciplinary methods, including optical sensing technologies for real-time monitoring and data acquisition, and intelligent grid management, the framework supports the development of smart cities, where energy systems must be sustainable, resilient, and efficient. Case studies on a modified IEEE 33-bus system demonstrate the framework's ability to achieve significant cost savings - up to 9% compared to deterministic methods - while improving reliability by 15%. Sensitivity analysis further reveals the framework's robustness to variations in scenario numbers and distributions. This scalable and computationally efficient approach provides a practical solution for real-world power system planning, particularly in the context of smart cities transitioning toward higher renewable penetration and intelligent energy infrastructures. Future research directions include exploring advanced scenario generation techniques and extending the framework to dynamic, multi-period planning under evolving uncertainties. © 2025 The Authors.
Author Keywords	Computer simulation; distribution network planning; Monte Carlo methods; optical sensing; stochastic optimization

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