Research on multi-stage optimization planning of power internet of things based on seagull optimization algorithm

The Power Internet of Things (PIoT) is a significant technology for realizing the transformation of future energy systems, with the Integrated Energy System (IES) playing a crucial role in realizing the value of PIoT. Traditional IES planning methods typically focus on a single-stage planning approach and involve complex solution models, often resulting in inefficient equipment configurations and resource wastage. This study proposes a multi-stage IES planning method aimed at enhancing both energy efficiency and the economic performance of IES. The method models the IES based on electric, gas, and thermal buses, considering the coupling, storage, and conversion of multiple energy sources. A range of constraints, such as energy coupling, equipment capacity, and energy purchases, are considered. The planning cycle is divided into multiple stages, and an economic model is developed that accounts for both system investment and operating costs. Given the complexity of the multi-stage planning model, the Seagull Optimization Algorithm (SOA) is introduced to solve the problem. The SOA leverages its strong global and local search capabilities to determine the optimal capacity configuration at each stage. The comparison of the single-stage planning method by a calculation example proves the economic advantage of the multi-stage planning scheme and effectiveness of SOA.