Impact Analysis and Optimal Placement of Distributed Energy Resources in Diverse Distribution Systems for Grid Congestion Mitigation and Performance Enhancement

• Published in: Electronics (Basel) Vol. 14; no. 10; p. 1998
• Main Authors: Iqbal, Hasan, Stevenson, Alexander, Sarwat, Arif I.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 14.05.2025
• Subjects: Alternative energy sources; Analysis; Batteries; Business metrics; Congestion; Electric power loss; Electric transformers; Electric vehicles; Electricity distribution; Energy consumption; Energy distribution; Energy resources; Energy sources; Impact analysis; Linear programming; Modular design; Monte Carlo method; Monte Carlo simulation; Network topologies; Optimization; Optimization techniques; Overloading; Peak load; Performance evaluation; Photovoltaic cells; Probability theory; Real time; Renewable resources; Simulation methods; Test systems; Violations
• DOI: 10.3390/electronics14101998
• ISSN: 2079-9292

The integration of Distributed Energy Resources (DERs) such as photovoltaic (PV) systems, battery energy storage systems (BESSs), and electric vehicles (EVs) introduces new challenges to distribution networks despite offering opportunities for decarbonization and grid flexibility. This paper proposes a scalable simulation-based framework that combines deterministic nodal hosting capacity analysis with probabilistic Monte Carlo simulations to evaluate and optimize DER integration in diverse feeder types. The methodology is demonstrated using the IEEE 13-bus and 123-bus test systems under full-year time-series simulations. Deterministic hosting capacity analysis shows that individual nodes can accommodate up to 76% of base load from PV sources, while Monte Carlo analysis reveals a network-wide average hosting capacity of 62%. Uncoordinated DER deployment leads to increased system losses, overvoltages, and thermal overloads. In contrast, coordinated integration achieves up to 38.7% reduction in power losses, 25% peak demand shaving, and voltage improvements from 0.928 p.u. to 0.971 p.u. Additionally, congestion-centric optimization reduces thermal overload indices by up to 65%. This framework aids utilities and policymakers in making informed decisions on DER planning by capturing both spatial and stochastic constraints. Its modular design allows for flexible adaptation across feeder scales and configurations. The results highlight the need for node-specific deployment strategies and probabilistic validation to ensure reliable, efficient DER integration. Future work will incorporate optimization-driven control and hardware-in-the-loop testing to support real-time implementation.