为了提高户用热泵直流微电网运行时的经济性与蓄电池使用寿命，建立了户用热泵直流微电网多场景联合优化模型，综合考虑了发电成本、放电成本、购电成本、售电收入、功率平衡约束及充放电时间约束等因素。以成本最低与蓄电池最优荷电状态为目标函数，利用改进的分群自适应粒子群优化算法平衡多目标优化的局部最优与全局最优问题。通过对比分析仿真结果，发现在不同场景下，改进粒子群算法的表现均优于其他算法。尤其在光伏发电量充足的晴天，该算法的运行成本比传统粒子群算法低8.8%，比遗传算法低24%，比线性规划算法低13.9%。结果验证了改进粒子群算法在降低户用热泵微电网并网模式下日常运行成本方面的有效性。

In order to improve the economy and battery service life during the operation of household heat pump DC microgrid, a multi-scenario joint optimization model of household heat pump DC microgrid was established, which taked into account the factors of power generation cost, discharge cost, power purchase cost, power sales revenue, power balance constraints, and charging/discharging time constraints. Taking the lowest cost and the optimal battery charging state as the objective function, the improved swarm adaptive particle swarm optimization algorithm was used to balance the local optimization and global optimization problems of multi-objective optimization. By comparing and analyzing the simulation results, it is found that the improved particle swarm algorithm outperforms the other algorithms in different scenarios. Especially in sunny days with sufficient PV power generation, the operation cost of this algorithm is 8.8% lower than the traditional particle swarm algorithm, 24% lower than the genetic algorithm, and 13.9% lower than the linear programming algorithm. The results validate the effectiveness of the improved particle swarm algorithm in reducing the daily operating cost in the grid-connected mode of household heat pump microgrid.