Tue, Sep 23, 2025
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Sri Sairam Engineering College
Abstract: (23 Views)
The growing demand for electricity, coupled with challenges such as peak load and network congestion, has necessitated the development of effective energy management strategies. This study presents an optimized framework that leverages demand response (DR) programs alongside electric vehicle (EV) routing to alleviate stress on distribution networks. The proposed approach emphasizes both participating and non-participating consumers to ensure equitable benefits across the network. By integrating EV routing within the DR scheme, the study addresses dynamic load distribution, especially during critical peak periods. To validate the proposed approach, simulation studies were performed on the IEEE 33-bus distribution network under both normal and increased loading scenarios. An incremental learning-based model was employed to iteratively enhance the system's adaptability and performance. Results demonstrate that the coordinated application of DR programs and EV routing significantly reduces peak demand and improves congestion management, thereby contributing to a more resilient and efficient power distribution system.
Keywords— Congestion management, Incremental learning, Peak load reduction, Demand response, Electric vehicle routing
The growing demand for electricity, coupled with challenges such as peak load and network congestion, has necessitated the development of effective energy management strategies. This study presents an optimized framework that leverages demand response (DR) programs alongside electric vehicle (EV) routing to alleviate stress on distribution networks. The proposed approach emphasizes both participating and non-participating consumers to ensure equitable benefits across the network. By integrating EV routing within the DR scheme, the study addresses dynamic load distribution, especially during critical peak periods. To validate the proposed approach, simulation studies were performed on the IEEE 33-bus distribution network under both normal and increased loading scenarios. An incremental learning-based model was employed to iteratively enhance the system's adaptability and performance. Results demonstrate that the coordinated application of DR programs and EV routing significantly reduces peak demand and improves congestion management, thereby contributing to a more resilient and efficient power distribution system.
Keywords— Congestion management, Incremental learning, Peak load reduction, Demand response, Electric vehicle routing
The growing demand for electricity, coupled with challenges such as peak load and network congestion, has necessitated the development of effective energy management strategies. This study presents an optimized framework that leverages demand response (DR) programs alongside electric vehicle (EV) routing to alleviate stress on distribution networks. The proposed approach emphasizes both participating and non-participating consumers to ensure equitable benefits across the network. By integrating EV routing within the DR scheme, the study addresses dynamic load distribution, especially during critical peak periods. To validate the proposed approach, simulation studies were performed on the IEEE 33-bus distribution network under both normal and increased loading scenarios. An incremental learning-based model was employed to iteratively enhance the system's adaptability and performance. Results demonstrate that the coordinated application of DR programs and EV routing significantly reduces peak demand and improves congestion management, thereby contributing to a more resilient and efficient power distribution system.
Keywords: Congestion management, Incremental learning, Peak load reduction, Demand response, Electric vehicle routing
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