Two-Layer Optimization of Active Distribution Network Based on Electric Vehicle Incentive Scheduling

With the gradual implementation of the “dual carbon” target policy, traditional fuel vehicles are transitioning to electric vehicles. Simultaneously, there has been substantial growth in new energy generation technologies and the proliferation of electric vehicles, leading to a significant increase in distributed power sources connected to active distribution networks. Active distribution networks have become a focal point in today's power systems due to these developments and are seen as a future trend in grid development. Therefore, effectively managing the integration of these new energy sources into the grid is crucial for ensuring safe and reliable voltage operation. To address challenges such as the impact of unregulated charging of electric vehicles on the distribution network's peak and valley load differentials, this paper proposes a dual-layer optimization strategy. This strategy integrates economic dispatch optimization and reactive power optimization of the distribution network, considering both the network's economic efficiency and security. The simulation utilizes a second-order cone algorithm and employs the IEEE 33-node system as a basis to construct and analyze three distinct scenarios for comparative evaluation. Key metrics such as distribution network losses and voltage quality are examined to validate the proposed model's effectiveness and the algorithm's capability in solving the dual-layer optimization. The findings provide theoretical support for optimal distribution network scheduling, demonstrating the rationale and effectiveness of the proposed model and its associated algorithm. This research contributes to advancing the understanding and practical application of managing active distribution networks amidst the increasing integration of electric vehicles and renewable energy sources.