Optimizing Water-Light Complementary Systems for the Complex Terrain of the Southwestern China Plateau Region: A Two-Layer Model Approach

Abstract

This study aimed to optimize the real-time, short-term dispatch of water-light complementary systems in plateau areas. A two-layer nested improved particle swarm optimization-stepwise optimization algorithm trial (IPSO-SOAT) model was devised to address the challenges posed by the intermittent, volatile, and random characteristics of renewable energy, leading to difficulties in renewable energy consumption and severe power cuts. The model, was employed to optimize the load distribution of complementary system power stations. The outer layer of the model employs an improved particle swarm optimization algorithm to introduce uncertainty and enhance prediction accuracy. Additionally, regional optimization and robust optimization were incorporated to improve prediction reliability. The objective function was aimed at minimizing the residual load variance. The inner layer of the model employs a stepwise optimization algorithm, coupled with a two-dimensional coding strategy for the hydropower unit, to optimize the operating status of the hydropower station unit. The objective function in this layer minimizes flow consumption. A water-light complementary system was comprehensively analyzed in the context of the southwestern plateau region, considering the complex terrain characteristics. By comparing three scenarios, the superiority and flexibility of the two-level nested model were visualized. The proposed double-layer nesting model minimizes energy and natural resource consumption while ensuring sustainability, resulting in a reduction of 15,644.265 tons of carbon dioxide emissions per year. This technological innovation makes a significant contribution to sustainable development.