Residential Demand Response Scheduling Under Hybrid Tariffs Using Novel Multi-Objective Chemical Reaction Optimization Algorithm

• Published in: IEEE access Vol. 12; pp. 135185 - 135206
• Main Authors: Cheng, Zheng, Lei, Weidong, Zhang, Shibohua
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bi-objective optimization; Chemical reactions; Cost analysis; Costs; Decoding; Demand response; Electrical loads; Electricity; Electricity consumption; Energy management; home appliance scheduling; Home appliances; Household appliances; hybrid pricing tariffs; Multiple objective analysis; novel chemical reaction optimization algorithm; Operators (mathematics); Optimization; Peak load; Pricing; Residential energy; Schedules; Scheduling; Smart buildings; Smart homes; Tariffs; Task scheduling; Time of use electricity pricing; Working hours
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2024.3462470

The scheduling of smart home appliance electricity tasks can effectively distribute power loads and enhance the operational efficiency and stability of power grids. Additionally, effectively shifting the working hours of household appliances to off-peak hours with lower electricity prices could reduce the cost of household electricity consumption. Therefore, we aim to study a bi-objective household appliance scheduling problem under time-of-use (TOU) and threshold-based pricing tariffs, where the objectives are to simultaneously minimize the total electricity usage cost and peak load within a day. A bi-objective mathematical model is first presented for this problem. To the best of our knowledge, this is the first study to propose a novel bi-objective chemical reaction optimization (CRO) algorithm with problem-specific encoding and decoding schemes, as well as molecular reaction operators to solve the studied problem. Finally, the computational results of well-known multi-objective optimization benchmark instances and case study were reported and analyzed. The results indicate that our proposed approach achieves a better balance between exploitation and exploration, yields high-quality trade-offs between reducing the electricity cost and peak load, and outperforms existing algorithms in terms of solution quality and diversity.