A bi-level cooperating optimization for AC/DC power systems considering renewable energy integration

The integration of renewable energy on a large scale into the power system presents a significant challenge in ensuring the secure and cost-efficient operation of the system. Considering the uncertainty associated with wind and photovoltaic power, this paper develops a bi-level cooperating optimization strategy for AC-DC hybrid systems integrated with renewable energy generation. Firstly, to characterize the uncertainty of wind and photovoltaic power, the Latin hypercube sampling and the Kantorovich distance-based scenario reduction methods are employed to obtain the typical daily outputs of wind and photovoltaic power. Next, a bi-level multi-objective comprehensive optimization model is established to minimize the active power network loss and voltage deviation during system operation. To solve the bi-level optimization model, the proposed approach presents a multi-objective hybrid algorithm that combines the traditional particle swarm and whale algorithms, thereby enhancing the optimization capability of the algorithm through the incorporation of a whale enveloping search strategy into particles. Finally, the simulation results explain the effectiveness and practicability of the proposed strategy and methods, as well as the superiority of the proposed algorithm.