基于改进NSGA-II算法的公共建筑江水源热泵系统集成多目标优化研究

以集成江水源热泵供能系统的公共建筑为对象,建立建筑供能成本最低、非舒适性时间最短、热泵用电峰谷比最小的多目标函数,基于动态能耗瞬时模拟技术和改进非支配排序遗传算法联合求解帕累托前沿,引入熵权优劣解距离法评价最优决策解;以夏热冬冷地区办公建筑为例,验证优化方法的可行性和最优设计方案。结果显示：最优方案与基准方案相比,建筑供能成本增加2.6%,非舒适性时间减少3.7%,热泵用电峰谷比降低90.6%;运行成本虽然稍有增加,但舒适性得到了提升,且用电峰谷波动性得到较大改善。

Integrated Multi-Objective Optimization of River Source Heat Pump System in Office Buildings Based on Improved NSGA-II Algorithm

Taking a public building integrated with the river water source heat pump energy supply system as the object, a multi-objective function with the lowest building energy supply cost, the shortest non-comfort time, and the lowest peak-to-valley ratio of heat pump electricity consumption was established. The Pareto front was solved jointly based on the dynamic energy consumption transient simulation technology and the improved non-dominated sorting genetic algorithm, and the optimal Decision-making solution was evaluated by the Entropy-weight solution distance method. Taking an office building in a hot summer and cold winter area as an example, the feasibility of the optimization method and the optimal design scheme were verified. Results showed as follows: compared with the reference scheme, the building energy supply cost of the optimal scheme increased by 2.6%, the non-comfort time decreased by 3.7%, and the peak-to-valley ratio of heat pump electricity consumption reduced by 90.6%; although the operating cost increased slightly, the comfort was improved, and the peak-to-valley fluctuation of electricity consumption was greatly improved.