Abstract:

As part of the strategic push toward achieving carbon neutrality, the imperative to integrate renewable energy into active distribution networks (ADNs) on a large scale has become a priority. However, the traditional centralized control methods are hamstrung by dispatch control strategies and data interchange modes, falling short in accommodating the needs for distributed energy assimilation and meeting the economic operation goals of distribution networks. In response, this work introduces an innovative regional multi-agent autonomous collaborative optimization approach for ADNs, grounded in Analytical Target Cascading (ATC). This approach treats flexible loads, distributed energy resources, and storage systems as controllable unit agents. It aligns with the overall economic optimization objectives of the distribution network and the localized autonomous optimization requirements of microgrid domains, framing a dispatch architecture structured around "ADN entity-node entity-controlled unit entity." Leveraging ATC to handle the inter-agent shared interactive information, the approach effectively separates complex system hierarchies into primary systems and subsystems. This separation facilitates a synergistic optimization that respects both the comprehensive and specific regional goals. The method's robustness and efficiency are substantiated through the development and testing within D9M2 and IEEE 33 node distribution systems, demonstrating its validity in practical scenarios.

Key words: active distribution network, multi-agent collaboration, analytical target cascading, distributed generation, regional autonomy