College of Engineering, Huazhong Agricultural University, Wuhan, 430070, China.
Sci Rep. 2021 Nov 4;11(1):21647. doi: 10.1038/s41598-021-00732-6.
With the development of park-level agricultural, agricultural production and household electricity fusion, it is of great significance to promote users to actively respond to power consumption plan based on their own habits. In this paper, a multi-objective household intelligent power consumption optimization model is proposed from two aspects of economy and comfort. Firstly, the operating constraints of interruptible loads and non-interruptible loads were established based on the working characteristics of various household appliances. Then, the expenditure model was constructed to take into account the electricity sales situation of surplus electricity generated by photovoltaic, and a three-layer index system quantifying the influence of user preference on comfort level was constructed. The preference coefficient was determined by analytic hierarchy process, which was used to construct the users' comfort level model. Finally, the multi-objective particle swarm optimization algorithm was applied to obtain optimization results. Considering the seasonal difference, the simulation showed that this model minimized the expenditure and increased the comfort level during summer and winter by 26.0% and 27.5% respectively.
随着园区级农电融合农业生产和家庭电力融合的发展，根据用户自身习惯积极响应用电计划具有重要意义。本文从经济和舒适两个方面提出了一种多目标家庭智能用电优化模型。首先，根据各种家用电器的工作特点，建立了可中断负荷和不可中断负荷的运行约束条件。然后，构建了支出模型，考虑了光伏产生的剩余电量的售电情况，并构建了一个三层指标体系，量化用户偏好对舒适度的影响。通过层次分析法确定偏好系数，构建用户舒适度模型。最后，应用多目标粒子群优化算法得到优化结果。考虑到季节性差异，仿真结果表明，该模型在夏季和冬季分别将支出降低了 26.0%和 27.5%，同时提高了舒适度。
