邻域自适应粒子群算法求解地源热泵区域能源系统鲁棒优化调度问题

Neighborhood adaptive particle swarm optimization algorithm for robust optimization dispatch of the ground source heat pump district energy system

针对地源热泵区域能源系统中冷热负荷和机组效能的不确定性,本文提出了一种考虑双重不确定性的鲁棒优化调度方法.首先,基于多面体不确定模型描述调度模型中的鲁棒变量.然后,针对建筑冷热负荷不确定性,采用对偶原理将双层优化模型等价为单层优化模型;对于机组效能不确定性,采用场景法进行分析.最后,采用多目标优化约束处理方法处理鲁棒优化调度模型中的约束条件.同时,为更加高效、准确求解所构建的优化调度模型,提出了一种邻域自适应粒子群优化算法(NAPSO).实验结果表明,在制冷和制热工况下,与经验运行策略相比,本文所提方法可分别减少7.22%和5.55%的系统运行成本,是一种解决地源热泵区域能源系统鲁棒优化调度的有效方法.

Aiming at the uncertainty of cooling/heating load and the uncertainty of performance of unit in the ground source heat pump district energy system,a robust optimal dispatch method considering double uncertainty is proposed.First,the robust variables in the model are described based on the polyhedral uncertainty model.Then,in view of the uncertainty of building cooling/heating load,the bi-level optimization model is equivalent to the single level optimization model by the duality principle,and the scenario method is used to analyze the uncertainty of performance of ground source heat pump unit.Finally,a multi-objective optimization constraint processing method is used to deal with the constraints in the robust optimal scheduling model.Meanwhile,in order to efficiently and accurately solve the optimal scheduling model of the ground source heat pump regional energy system,the neighborhood adaptive particle swarm optimization(NAPSO)algorithm was proposed.The experimental results show that the optimal scheduling scheme obtained by the NAPSO algorithm can save 7.22%in cooling conditions and 5.55%in heating conditions in terms of operating costs compared with the empirical operation method,and the proposed method is an effective robust optimal dispatch method for ground source heat pump district energy system.