Abstract: Aiming at the challenge of balancing economic and environmental objectives in the multi-objective optimal scheduling of virtual power plants(VPPs), this paper proposes an optimization method based on the multi-objective artificial hummingbird algorithm(MOAHA). A scheduling model is established with the objectives of minimizing both operational cost and carbon emissions. The multi-objective artificial hummingbird algorithm is introduced to solve this model, leveraging its three foraging behaviors combined with an external archive and a dynamic crowding distance strategy to enhance the convergence and distribution uniformity of the obtained solutions. furthermore, the entropy weight-TOPSiS comprehensive evaluation method is employed to select the optimal compromise solution from the Pareto front. Experimental results demonstrate that the proposed method achieves a widely distributed Pareto solution set. The comprehensive optimal scheme yields an operational cost of 1.544 908 million yuan and carbon emissions of 19.464 0 t. Compared to the solutions obtained by the multi-objective grey wolf optimizer and the multi-objective genetic algorithm, the proposed method reduces operational cost by 2.57% and 33.02% respectively, and reduces carbon emissions by 65.2% and 72.1% respectively. This validates the effectiveness and superiority of the proposed method in simultaneously addressing economic and environmental goals. Additionally, while a purely economic-oriented scheme saves 59.38% in cost compared to the comprehensive optimal scheme, it results in a 465.19% increase in carbon emissions.

Key words: virtual power plant, multi-objective artificial hummingbird algorithm, entropy weight-TOPSiS decision-making, optimal scheduling