基于多目标粒子群算法的SOFC/MGT混合发电系统热电优化研究

Research on Thermoelectric Optimization of SOFC/MGT Hybrid Power Generation System Based on Multi-Objective Particle Swarm Optimization Algorithm

摘要: 固体氧化物燃料电池与微型燃气轮机构成的混合发电系统因其高效率和低排放特性在分布式发电系统中具有广泛应用前景。然而，该系统存在强耦合，非线性等特点，特别是在复杂工况下运行时，其性能优化问题尤为突出。为实现系统在不同负载条件下的高效稳定运行，本文提出了一种基于多目标粒子群优化(MOPSO)算法的热电协同优化方法，以系统发电效率和输出电压为双重优化目标。通过建立详细的动态优化模型，选取关键决策变量并设定合理的边界约束，结合MOPSO算法探索Pareto最优解集，最终获得不同工况下的最优运行参数组合。研究结果表明，该方法能显著提升系统在典型运行条件下的能效表现，系统效率最高可接近65%，输出电压保持在0.64 V至0.74 V区间，为SOFC/MGT系统的工程优化与智能控制提供了理论依据与技术支持。

Abstract: The hybrid power generation system composed of solid oxide fuel cell and micro gas turbine has a wide application prospect in distributed power generation system because of its high efficiency and low emission characteristics. However, the system has the characteristics of strong coupling and nonlinearity, especially in complex operating conditions, its performance optimization problem is particularly prominent. In order to realize the efficient and stable operation of the system under different load conditions, this paper proposes a thermoelectric collaborative optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, which takes the power generation efficiency and output voltage of the system as the dual optimization objectives. By establishing a detailed dynamic optimization model, selecting key decision variables and setting reasonable boundary constraints, and combining with MOPSO algorithm to explore the Pareto optimal solution set, the optimal operation parameter combination under different working conditions is finally obtained. The research results show that this method can significantly improve the energy efficiency performance of the system under typical operating conditions, the maximum system efficiency can be close to 65%, and the output voltage is kept in the range of 0.64 V to 0.74 V, which provides a theoretical basis and technical support for the engineering optimization and intelligent control of sofcgmgt system.