Residential Demand Response Scheduling Under Hybrid Tariffs Using Novel Multi-Objective Chemical Reaction Optimization Algorithm Article Swipe
Zheng Cheng
,
Weidong Lei
,
Shibohua Zhang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1109/access.2024.3462470
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/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.
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- Type
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- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2024.3462470
- OA Status
- gold
- Cited By
- 1
- References
- 54
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- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4402569333Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2024.3462470Digital Object Identifier
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Residential Demand Response Scheduling Under Hybrid Tariffs Using Novel Multi-Objective Chemical Reaction Optimization AlgorithmWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
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2024-01-01Full publication date if available
- Authors
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Zheng Cheng, Weidong Lei, Shibohua ZhangList of authors in order
- Landing page
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https://doi.org/10.1109/access.2024.3462470Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2024.3462470Direct OA link when available
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Computer science, Demand response, Scheduling (production processes), Mathematical optimization, Engineering, Mathematics, Electrical engineering, ElectricityTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.distribute | 10 |
| abstract_inverted_index.diversity. | 184 |
| abstract_inverted_index.efficiency | 17 |
| abstract_inverted_index.knowledge, | 98 |
| abstract_inverted_index.objectives | 67 |
| abstract_inverted_index.scheduling | 1, 56 |
| abstract_inverted_index.trade-offs | 165 |
| abstract_inverted_index.well-known | 136 |
| abstract_inverted_index.effectively | 9, 24 |
| abstract_inverted_index.electricity | 6, 37, 45, 74, 169 |
| abstract_inverted_index.operational | 16 |
| abstract_inverted_index.outperforms | 175 |
| abstract_inverted_index.time-of-use | 59 |
| abstract_inverted_index.bi-objective | 53, 84, 108 |
| abstract_inverted_index.consumption. | 46 |
| abstract_inverted_index.exploitation | 160 |
| abstract_inverted_index.exploration, | 162 |
| abstract_inverted_index.high-quality | 164 |
| abstract_inverted_index.mathematical | 85 |
| abstract_inverted_index.optimization | 111, 138 |
| abstract_inverted_index.Additionally, | 23 |
| abstract_inverted_index.computational | 133 |
| abstract_inverted_index.simultaneously | 70 |
| abstract_inverted_index.multi-objective | 137 |
| abstract_inverted_index.threshold-based | 62 |
| abstract_inverted_index.problem-specific | 115 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.60934781 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |