With the continuous increase in the grid-connected capacity of wind power and photovoltaic power, their inherent volatility and intermittency make the net load fluctuation in the power system become larger. To measure the matching degree between the contours of the new energy curve and the load curve, Euclidean distance characterizing the numerical matching of renewable energy-load and the Spearman’s rank correlation coefficient characterizing shape matching are combined to form a source-load matching index, and a two-stage optimization model is proposed. In the first stage, energy storage and adjustable load electrolytic aluminum are used to adjust the power consumption of the load. Taking minimizing the source-load matching index and minimizing the adjustment cost of energy storage and electrolytic aluminum load as the optimization goals respectively, and the multi-objective particle swarm optimization algorithm is used for solution. In the second stage, according to the optimized load curve and new energy curve in the first stage, taking the minimum sum of the operating cost of conventional units, the cost of wind and photovoltaic curtailment and the cost of load shedding as the optimization goal, and the Gurobi solver in MATLAB is used for solution.