Multiobjective optimization decision-making on electricity procurement for grid companies considering imbalanced funds under a dual-track system

With the deepening reform of the electricity market, grid companies face multiple complex economic and policy constraints in the process of power procurement and sales. This study proposed a new optimization model for electricity procurement by grid companies, addressing the challenges arising from the imbalanced scale of dual-track system funds. The proposed model integrated the cost considerations of grid companies in electricity trading with the issue of dualtrack system funding imbalance. A multi-objective decision-making framework was developed, aiming to minimize the procurement costs for end-users while controlling the scale of the dual-track system imbalance funds. The optimization of the grid company's procurement strategy was conducted under constraints including user-side demand and mediumto-long-term procurement requirements. The model was solved using an improved particle swarm optimization (PSO) algorithm, providing an optimized solution for grid companies in procuring electricity. Simulation results confirm that the proposed model can effectively reduce electricity procurement costs for grid companies while controlling the scale of the dual-track imbalance funds. The multi-objective optimization decision-making on electricity procurement for grid companies under a dual-track system is effective and feasible.