A multi-agent autonomous power distribution network dynamic restoration method based on principal-agent game

Abstract

This invention discloses a dynamic recovery method for multi-agent autonomous distribution networks based on master-slave game theory. The method addresses issues such as data dispersion, complex decision-making hierarchies, and strong privacy protection requirements in multi-agent distribution networks under extreme events by modeling distribution network operators and autonomous entities such as microgrids and virtual power plants into a master-slave game relationship. A two-layer game model for dynamic distribution network recovery is formed by constructing an upper-level optimization model for centralized units and a lower-level optimization model for autonomous entity units. This model is then equivalently transformed into a single-layer mixed-integer linear programming model using strong duality theory and KKT conditions. A commercial optimization solver is employed to obtain the globally optimal recovery strategy. This invention achieves global coordination and optimality in the distribution network recovery process while ensuring independent decision-making and data privacy for each autonomous entity, thus improving the recovery efficiency and system resilience of complex distribution networks.

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