Optimization strategy for green wind energy storage systems based on natural resources and enterprise load

Abstract

This study employs a hybrid NRBO-ICEEMDAN algorithm (combining Newton-Raphson Based Optimization and Improved Complete Ensemble Empirical Mode Decomposition) to optimize wind-storage strategies for enterprise energy systems, achieving significant cost reductions and profit growth. The results show that under the optimization model of maximum load matching rate, cost, and benefit, the cost of wind energy equipment accounts for 61.31 % of the total cost and the electricity sales revenue is 74.6434 million yuan under the operation of a single wind energy equipment. The proportion of peak shaving, valley filling, and carbon reduction benefits is not high; Under the operation of a single energy storage system, the cost of energy storage equipment accounts for 39.27 % of the total cost, with a revenue of 12.5991 million yuan, highly concentrated on the peak shaving and valley filling functions of the energy storage system. Considering the overall operation, the total cost accounts for 83.7 % and the revenue is as high as 114655500 yuan, which improves the comprehensive efficiency of the energy system. At the same time, with the increase of operation time, the expansion and maintenance investment of wind energy equipment and energy storage equipment, the comprehensive operation strategy benefits are more considerable. Research has shown that the optimization strategy of green energy wind energy storage plays a decisive role in reducing total costs and increasing benefits. This study provides a new methodology and decision support for optimizing green energy wind energy storage strategies for natural resources and enterprise loads, emphasizing the importance of comprehensive consideration of economic and environmental goals.