Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power Systems Article Swipe
Daniel Fernández Valderrama
,
Juan Ignacio Guerrero
,
Carlos León
,
Michela Robba
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17215293
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/en17215293
Uncertainty is an important subject in optimization problems due to the unpredictable nature of real variables in the power system area, which can condition the solution’s accuracy. The effective modelling of stochastic variables can contribute to the reduction in losses in the system under evaluation and facilitate the implementation of an effective response in advance. To model uncertainty variables, the most extended technique is the scenario generation (SG) method. This method evaluates possible combinations of complete curves. Classical scenario generation methods are founded on probability distributions or robust stochastic optimization. This paper proposes a novel approach for constructing scenarios using the Ant Colony Optimization (ACO) algorithm, referred to as ACO-SG. This methodology does not require a previous statistical study of uncertainty data to generate new scenarios. A historical dataset and the desired number of scenarios are the inputs inserted into the algorithm. In the case study, the algorithm used historical data from the Savona Campus Smart Polygeneration Microgrid of the University of Genoa. The approach was applied to generate scenarios of photovoltaic generation and building consumption. The low values of the Euclidean distance were used in order to check the validity of the scenarios. Moreover, the error deviation of the scenarios generated with the goal of daily power were 1.77% and 0.144% for the cases of PV generation and building consumption, respectively. The different results for both cases are explained by the characteristics of the specific cases. Despite these different results, both were significantly low, which indicates the capability of the algorithm to generate any kind of feature within curves and its adaptability to any case of SG.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/en17215293
- OA Status
- gold
- Cited By
- 2
- References
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https://doi.org/10.3390/en17215293Digital Object Identifier
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Scenario Generation Based on Ant Colony Optimization for Modelling Stochastic Variables in Power SystemsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-10-24Full publication date if available
- Authors
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Daniel Fernández Valderrama, Juan Ignacio Guerrero, Carlos León, Michela RobbaList of authors in order
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https://doi.org/10.3390/en17215293Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/en17215293Direct OA link when available
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Ant colony optimization algorithms, Stochastic optimization, Ant colony, Mathematical optimization, Metaheuristic, Computer science, ANT, Mathematics, Computer networkTop concepts (fields/topics) attached by OpenAlex
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.low, | 244 |
| abstract_inverted_index.most | 60 |
| abstract_inverted_index.real | 14 |
| abstract_inverted_index.used | 148, 184 |
| abstract_inverted_index.were | 183, 208, 242 |
| abstract_inverted_index.with | 202 |
| abstract_inverted_index.(ACO) | 104 |
| abstract_inverted_index.1.77% | 209 |
| abstract_inverted_index.Smart | 155 |
| abstract_inverted_index.area, | 20 |
| abstract_inverted_index.cases | 214, 227 |
| abstract_inverted_index.check | 188 |
| abstract_inverted_index.daily | 206 |
| abstract_inverted_index.error | 196 |
| abstract_inverted_index.model | 56 |
| abstract_inverted_index.novel | 94 |
| abstract_inverted_index.order | 186 |
| abstract_inverted_index.paper | 91 |
| abstract_inverted_index.power | 18, 207 |
| abstract_inverted_index.study | 118 |
| abstract_inverted_index.these | 238 |
| abstract_inverted_index.under | 43 |
| abstract_inverted_index.using | 99 |
| abstract_inverted_index.which | 21, 245 |
| abstract_inverted_index.0.144% | 211 |
| abstract_inverted_index.Campus | 154 |
| abstract_inverted_index.Colony | 102 |
| abstract_inverted_index.Genoa. | 162 |
| abstract_inverted_index.Savona | 153 |
| abstract_inverted_index.cases. | 236 |
| abstract_inverted_index.curves | 259 |
| abstract_inverted_index.inputs | 137 |
| abstract_inverted_index.losses | 39 |
| abstract_inverted_index.method | 70 |
| abstract_inverted_index.nature | 12 |
| abstract_inverted_index.number | 132 |
| abstract_inverted_index.robust | 87 |
| abstract_inverted_index.study, | 145 |
| abstract_inverted_index.system | 19, 42 |
| abstract_inverted_index.values | 178 |
| abstract_inverted_index.within | 258 |
| abstract_inverted_index.ACO-SG. | 109 |
| abstract_inverted_index.Despite | 237 |
| abstract_inverted_index.applied | 166 |
| abstract_inverted_index.curves. | 76 |
| abstract_inverted_index.dataset | 128 |
| abstract_inverted_index.desired | 131 |
| abstract_inverted_index.feature | 257 |
| abstract_inverted_index.founded | 82 |
| abstract_inverted_index.method. | 68 |
| abstract_inverted_index.methods | 80 |
| abstract_inverted_index.require | 114 |
| abstract_inverted_index.results | 224 |
| abstract_inverted_index.subject | 4 |
| abstract_inverted_index.advance. | 54 |
| abstract_inverted_index.approach | 95, 164 |
| abstract_inverted_index.building | 174, 219 |
| abstract_inverted_index.complete | 75 |
| abstract_inverted_index.distance | 182 |
| abstract_inverted_index.extended | 61 |
| abstract_inverted_index.generate | 123, 168, 253 |
| abstract_inverted_index.inserted | 138 |
| abstract_inverted_index.possible | 72 |
| abstract_inverted_index.previous | 116 |
| abstract_inverted_index.problems | 7 |
| abstract_inverted_index.proposes | 92 |
| abstract_inverted_index.referred | 106 |
| abstract_inverted_index.response | 52 |
| abstract_inverted_index.results, | 240 |
| abstract_inverted_index.scenario | 65, 78 |
| abstract_inverted_index.specific | 235 |
| abstract_inverted_index.validity | 190 |
| abstract_inverted_index.Classical | 77 |
| abstract_inverted_index.Euclidean | 181 |
| abstract_inverted_index.Microgrid | 157 |
| abstract_inverted_index.Moreover, | 194 |
| abstract_inverted_index.accuracy. | 26 |
| abstract_inverted_index.algorithm | 147, 251 |
| abstract_inverted_index.condition | 23 |
| abstract_inverted_index.deviation | 197 |
| abstract_inverted_index.different | 223, 239 |
| abstract_inverted_index.effective | 28, 51 |
| abstract_inverted_index.evaluates | 71 |
| abstract_inverted_index.explained | 229 |
| abstract_inverted_index.generated | 201 |
| abstract_inverted_index.important | 3 |
| abstract_inverted_index.indicates | 246 |
| abstract_inverted_index.modelling | 29 |
| abstract_inverted_index.reduction | 37 |
| abstract_inverted_index.scenarios | 98, 134, 169, 200 |
| abstract_inverted_index.technique | 62 |
| abstract_inverted_index.variables | 15, 32 |
| abstract_inverted_index.University | 160 |
| abstract_inverted_index.algorithm, | 105 |
| abstract_inverted_index.algorithm. | 141 |
| abstract_inverted_index.capability | 248 |
| abstract_inverted_index.contribute | 34 |
| abstract_inverted_index.evaluation | 44 |
| abstract_inverted_index.facilitate | 46 |
| abstract_inverted_index.generation | 66, 79, 172, 217 |
| abstract_inverted_index.historical | 127, 149 |
| abstract_inverted_index.scenarios. | 125, 193 |
| abstract_inverted_index.stochastic | 31, 88 |
| abstract_inverted_index.variables, | 58 |
| abstract_inverted_index.Uncertainty | 0 |
| abstract_inverted_index.methodology | 111 |
| abstract_inverted_index.probability | 84 |
| abstract_inverted_index.statistical | 117 |
| abstract_inverted_index.uncertainty | 57, 120 |
| abstract_inverted_index.Optimization | 103 |
| abstract_inverted_index.adaptability | 262 |
| abstract_inverted_index.combinations | 73 |
| abstract_inverted_index.constructing | 97 |
| abstract_inverted_index.consumption, | 220 |
| abstract_inverted_index.consumption. | 175 |
| abstract_inverted_index.optimization | 6 |
| abstract_inverted_index.photovoltaic | 171 |
| abstract_inverted_index.solution’s | 25 |
| abstract_inverted_index.distributions | 85 |
| abstract_inverted_index.optimization. | 89 |
| abstract_inverted_index.respectively. | 221 |
| abstract_inverted_index.significantly | 243 |
| abstract_inverted_index.unpredictable | 11 |
| abstract_inverted_index.Polygeneration | 156 |
| abstract_inverted_index.implementation | 48 |
| abstract_inverted_index.characteristics | 232 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| corresponding_author_ids | https://openalex.org/A5067521501 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I83816512 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.5299999713897705 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.69704631 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |