并网型风光储电解铝系统容量优化

Energy-intensive industries consume a considerable amount of energy and emit high levels of carbon dioxide, which places a significant burden on environmental protection. However, there is a possibility that the transformation of these industries into green and low-carbon operations may prove inadequate for addressing global climate change and achieving carbon neutrality goals. In order to address this challenge, this paper focuses on the load electrolytic aluminum production process and constructs a bi-level optimization model. The objective is to optimize the configuration of photovoltaic (PV), wind turbines (WT), and energy storage systems in order to maximize the utilization of renewable energy sources in aluminum production. The outer layer of the bi-level optimization model is designed to achieve maximum economic benefits, while the inner layer is set to identify the minimum energy storage configuration capacity. The Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is employed for the purpose of optimization analysis. Moreover, the model takes into account the flexible adjustability of electrolytic aluminum, treating it as a power-adjustable load in order to efficiently match the electricity demand of the production process with the generation capacity of renewable energy sources. The results demonstrate that the proposed optimization model not only achieves efficient local consumption of renewable energy but also reduces the investment cost and strengthens the capability of system to cope with the volatility of renewable energy generation by utilizing electrolytic aluminum as an adjustable resource. This has significant practical implications for the utilization of a high proportion of renewable energy in the electrolytic aluminum production process.