为合理高效利用生物质资源，考虑各微源单元的运行成本和环境成本，设计了一种基于改进生物质气化的冷热电联供微电网经济调度模型。针对麻雀搜索算法易陷入局部最优的问题，提出一种改进的麻雀搜索算法（improved sparrow search algorithm, ISSA）对所提出的模型进行求解。首先，使用正弦混沌图生成空间上均匀分布的早期麻雀种群。其次，加入互利学习机制并引入变异策略，以增强该领域个体之间的信息共享和全局搜索能力。最后通过对比ISSA、SSA、灰狼算法、鲸鱼算法和海洋捕食者算法的迭代结果，证明ISSA具有良好的寻优效果和稳定性。并通过典型仿真案例分析，验证了ISSA算法解决冷热电联供微电网经济调度问题的有效性。

In order to make rational and efficient use of biomass resources, considering the operating cost and environmental cost of each microsource unit, an economic dispatching model of combined cooling heating and power microgrid based on improved biomass gasification was designed. In order to solve the problem that the sparrow search algorithm is easy to fall into the local optimum, an improved sparrow search algorithm(ISSA) was proposed to solve the proposed model. First, a sine chaos map was used to generate spatially evenly distributed early sparrow populations. Secondly, a mutually beneficial learning mechanism was added and a mutation strategy was introduced to enhance the information sharing and global search ability among individuals in this field. Finally, by comparing the iterative results of ISSA, SSA, gray wolf algorithm, whale algorithm and marine predator algorithm, it is proved that ISSA has good optimization effect and stability. Through the analysis of typical simulation cases, the effectiveness of the ISSA algorithm in solving the economic dispatching problem of combined cooling, heating and power microgrid is verified.