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

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 redu...

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Vydáno v:IEEE access Ročník 12; s. 135185 - 135206
Hlavní autoři: Cheng, Zheng, Lei, Weidong, Zhang, Shibohua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Shrnutí: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3462470