Regional Frequency-Constrained Planning for the Optimal Sizing of Power Systems via Enhanced Input Convex Neural Networks Article Swipe
Yi Wang
,
Yujian Ye
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tste.2024.3524720
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/tste.2024.3524720
Large renewable penetration has been witnessed in power systems, resulting in reduced levels of system inertia and increasing requirements for frequency response services. There have been plenty of studies developing frequency-constrained models for power system security. However, most existing literature only considers uniform frequency security, while neglecting frequency spatial differences in different regions. To fill this gap, this paper proposes a novel planning model for the optimal sizing problem of power systems, capturing regional frequency security and inter-area frequency oscillations. Specifically, regional frequency constraints are first extracted via an enhanced input convex neural network (ICNN) and then embedded into the original optimisation for frequency security, where a principled weight initialisation strategy is adopted to deal with the gradient vanishing issues of non-negative weights in traditional ICNNs and enhance its fitting ability. An adaptive genetic algorithm with sparsity calculation and local search is developed to separate the planning model into two stages and effectively solve it iteratively. Case studies have been conducted on three different power systems to verify the effectiveness of the proposed frequency-constrained planning model in ensuring regional system security and obtaining realistic investment decisions.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/tste.2024.3524720
- OA Status
- green
- Cited By
- 2
- References
- 33
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405967975
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405967975Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/tste.2024.3524720Digital Object Identifier
- Title
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Regional Frequency-Constrained Planning for the Optimal Sizing of Power Systems via Enhanced Input Convex Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Yi Wang, Yujian YeList of authors in order
- Landing page
-
https://doi.org/10.1109/tste.2024.3524720Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2507.18102Direct OA link when available
- Concepts
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Sizing, Artificial neural network, Regular polygon, Computer science, Convex optimization, Power (physics), Electric power system, Mathematical optimization, Control theory (sociology), Mathematics, Artificial intelligence, Physics, Control (management), Art, Geometry, Quantum mechanics, Visual artsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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33Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.models | 31 |
| abstract_inverted_index.neural | 92 |
| abstract_inverted_index.plenty | 26 |
| abstract_inverted_index.search | 140 |
| abstract_inverted_index.sizing | 67 |
| abstract_inverted_index.stages | 150 |
| abstract_inverted_index.system | 14, 34, 179 |
| abstract_inverted_index.verify | 167 |
| abstract_inverted_index.weight | 108 |
| abstract_inverted_index.adopted | 112 |
| abstract_inverted_index.enhance | 127 |
| abstract_inverted_index.fitting | 129 |
| abstract_inverted_index.genetic | 133 |
| abstract_inverted_index.inertia | 15 |
| abstract_inverted_index.network | 93 |
| abstract_inverted_index.optimal | 66 |
| abstract_inverted_index.problem | 68 |
| abstract_inverted_index.reduced | 11 |
| abstract_inverted_index.spatial | 48 |
| abstract_inverted_index.studies | 28, 157 |
| abstract_inverted_index.systems | 165 |
| abstract_inverted_index.uniform | 42 |
| abstract_inverted_index.weights | 122 |
| abstract_inverted_index.However, | 36 |
| abstract_inverted_index.ability. | 130 |
| abstract_inverted_index.adaptive | 132 |
| abstract_inverted_index.embedded | 97 |
| abstract_inverted_index.enhanced | 89 |
| abstract_inverted_index.ensuring | 177 |
| abstract_inverted_index.existing | 38 |
| abstract_inverted_index.gradient | 117 |
| abstract_inverted_index.original | 100 |
| abstract_inverted_index.planning | 62, 146, 174 |
| abstract_inverted_index.proposed | 172 |
| abstract_inverted_index.proposes | 59 |
| abstract_inverted_index.regional | 73, 81, 178 |
| abstract_inverted_index.regions. | 52 |
| abstract_inverted_index.response | 21 |
| abstract_inverted_index.security | 75, 180 |
| abstract_inverted_index.separate | 144 |
| abstract_inverted_index.sparsity | 136 |
| abstract_inverted_index.strategy | 110 |
| abstract_inverted_index.systems, | 8, 71 |
| abstract_inverted_index.algorithm | 134 |
| abstract_inverted_index.capturing | 72 |
| abstract_inverted_index.conducted | 160 |
| abstract_inverted_index.considers | 41 |
| abstract_inverted_index.developed | 142 |
| abstract_inverted_index.different | 51, 163 |
| abstract_inverted_index.extracted | 86 |
| abstract_inverted_index.frequency | 20, 43, 47, 74, 78, 82, 103 |
| abstract_inverted_index.obtaining | 182 |
| abstract_inverted_index.realistic | 183 |
| abstract_inverted_index.renewable | 1 |
| abstract_inverted_index.resulting | 9 |
| abstract_inverted_index.security, | 44, 104 |
| abstract_inverted_index.security. | 35 |
| abstract_inverted_index.services. | 22 |
| abstract_inverted_index.vanishing | 118 |
| abstract_inverted_index.witnessed | 5 |
| abstract_inverted_index.decisions. | 185 |
| abstract_inverted_index.developing | 29 |
| abstract_inverted_index.increasing | 17 |
| abstract_inverted_index.inter-area | 77 |
| abstract_inverted_index.investment | 184 |
| abstract_inverted_index.literature | 39 |
| abstract_inverted_index.neglecting | 46 |
| abstract_inverted_index.principled | 107 |
| abstract_inverted_index.calculation | 137 |
| abstract_inverted_index.constraints | 83 |
| abstract_inverted_index.differences | 49 |
| abstract_inverted_index.effectively | 152 |
| abstract_inverted_index.penetration | 2 |
| abstract_inverted_index.traditional | 124 |
| abstract_inverted_index.iteratively. | 155 |
| abstract_inverted_index.non-negative | 121 |
| abstract_inverted_index.optimisation | 101 |
| abstract_inverted_index.requirements | 18 |
| abstract_inverted_index.Specifically, | 80 |
| abstract_inverted_index.effectiveness | 169 |
| abstract_inverted_index.oscillations. | 79 |
| abstract_inverted_index.initialisation | 109 |
| abstract_inverted_index.frequency-constrained | 30, 173 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.8540623 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |