【摘要】 针对新能源发电存在随机性、间歇性及初期投资成本高等问题，对面向船舶微电网的双层优化模型进行构建，本文的上层模型以系统初始投资成本最低、污染物排放最少为目标优化配置，下层模型以系统运行成本最低、系统出力波动最小为目标优化调度。提出黄金Halton-Levy麻雀算法（Golden Halton-Levy Sparrow Search Algorithm,GH-LSSA）求解上层配置模型，调用商业求解器CPLEX和YALMIP工具箱求解下层模型。针对船舶特殊工况，提出增益自整定PID偏航控制策略对调度进行优化。研究结果表明，与粒子群算法（Particle Swarm Optimization, PSO）相比，黄金Halton-Levy麻雀算法的单次航程运行成本比PSO算法低2.2万元，出力波动降低7.47 kW，在经济性、可靠性与环保性之间取得了最佳平衡，综合性能表现最优。使用增益自整定PID偏航控制策略的船舶微电网更适应船舶航行的特殊复杂工况，可靠性更高。更多还原

【Abstract】 New energy generation faces challenges such as randomness, intermittency, and high initial investment costs.To address these issues, a bi-level optimization model for ship microgrids was constructed. The upper-level model optimizes configuration with the objectives of minimizing initial investment costs and pollutant emissions, while the lower-level model optimizes scheduling with the goals of minimizing operational costs and power output fluctuations. The Golden Halton-Levy Sparrow Search Algorithm(GH-LSSA) was proposed to solve the upper-level configuration model, and the commercial solvers CPLEX and the YALMIP toolbox were employed to solve the lower-level model. For the special operational conditions of ships, a gain self-tuning PID yaw control strategy was introduced to optimize scheduling. The results demonstrate that,compared to the Particle Swarm Optimization(PSO) algorithm, the GH-LSSA reduces single-voyage operational costs by22,000 yuan and decreases power output fluctuations by 7.47 kW. It achieves the optimal balance among economy, reliability, and environmental friendliness, demonstrating the best overall performance. The ship microgrid utilizing the gain selftuning PID yaw control strategy proves more adaptable to the complex and unique operational conditions of maritime navigation, exhibiting higher reliability.更多还原