Operations Management of Power Interchange Networks Using Stochastic Programming Article Swipe
Tetsuya WAKUI
,
Taiki IDA
,
Ryohei Yokoyama
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1299/jsmeenv.2021.31.409
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1299/jsmeenv.2021.31.409
The power interchange among multiple consumers can reduce the operation cost because the generated power can be utilized effectively. Involving photovoltaic generation and electric vehicles with a large-capacity battery in the power interchange network has possibility to further enhance the reduction effect of the operation cost. However, the power output of photovoltaic panels and the driving time and daily distance of electric vehicles vary with uncertainty. In the present study, a stochastic operational planning problem of a power interchange network including photovoltaic panels and electric vehicles was formulated using mixed-integer linear programming. The contribution is to tolerate charging/discharging planning to/from electric vehicles as recourses to stochastic scenarios of input conditions. As a case study, the stochastic operation schedule of a residential power interchange network, consisting of photovoltaics panels, heat pump water-heating units, storage tanks, and battery units or electric vehicles, was planned. The result showed that stochastic scheduling provides an effective operation strategy to reduce the expected value of the operation cost under realized input data.
Related Topics
Concepts
Photovoltaic system
Stochastic programming
Schedule
Linear programming
Electric power
Computer science
Automotive engineering
Scheduling (production processes)
Integer programming
Battery (electricity)
Photovoltaics
Cost reduction
Mathematical optimization
Power (physics)
Reliability engineering
Engineering
Electrical engineering
Operations management
Economics
Mathematics
Operating system
Quantum mechanics
Management
Physics
Algorithm
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1299/jsmeenv.2021.31.409
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3176201175
All OpenAlex metadata
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https://openalex.org/W3176201175Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1299/jsmeenv.2021.31.409Digital Object Identifier
- Title
-
Operations Management of Power Interchange Networks Using Stochastic ProgrammingWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
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Tetsuya WAKUI, Taiki IDA, Ryohei YokoyamaList of authors in order
- Landing page
-
https://doi.org/10.1299/jsmeenv.2021.31.409Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1299/jsmeenv.2021.31.409Direct OA link when available
- Concepts
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Photovoltaic system, Stochastic programming, Schedule, Linear programming, Electric power, Computer science, Automotive engineering, Scheduling (production processes), Integer programming, Battery (electricity), Photovoltaics, Cost reduction, Mathematical optimization, Power (physics), Reliability engineering, Engineering, Electrical engineering, Operations management, Economics, Mathematics, Operating system, Quantum mechanics, Management, Physics, AlgorithmTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 42, 50, 60, 75, 107, 118, 125, 158 |
| abstract_inverted_index.or | 137 |
| abstract_inverted_index.to | 36, 95, 104, 153 |
| abstract_inverted_index.The | 0, 92, 142 |
| abstract_inverted_index.and | 22, 53, 57, 83, 134 |
| abstract_inverted_index.can | 6, 15 |
| abstract_inverted_index.has | 34 |
| abstract_inverted_index.the | 8, 12, 30, 39, 43, 47, 54, 67, 114, 155, 159 |
| abstract_inverted_index.was | 86, 140 |
| abstract_inverted_index.case | 112 |
| abstract_inverted_index.cost | 10, 161 |
| abstract_inverted_index.heat | 128 |
| abstract_inverted_index.pump | 129 |
| abstract_inverted_index.that | 145 |
| abstract_inverted_index.time | 56 |
| abstract_inverted_index.vary | 63 |
| abstract_inverted_index.with | 25, 64 |
| abstract_inverted_index.among | 3 |
| abstract_inverted_index.cost. | 45 |
| abstract_inverted_index.daily | 58 |
| abstract_inverted_index.data. | 165 |
| abstract_inverted_index.input | 108, 164 |
| abstract_inverted_index.power | 1, 14, 31, 48, 77, 121 |
| abstract_inverted_index.under | 162 |
| abstract_inverted_index.units | 136 |
| abstract_inverted_index.using | 88 |
| abstract_inverted_index.value | 157 |
| abstract_inverted_index.effect | 41 |
| abstract_inverted_index.linear | 90 |
| abstract_inverted_index.output | 49 |
| abstract_inverted_index.panels | 52, 82 |
| abstract_inverted_index.reduce | 7, 154 |
| abstract_inverted_index.result | 143 |
| abstract_inverted_index.showed | 144 |
| abstract_inverted_index.study, | 69, 113 |
| abstract_inverted_index.tanks, | 133 |
| abstract_inverted_index.units, | 131 |
| abstract_inverted_index.battery | 28, 135 |
| abstract_inverted_index.because | 11 |
| abstract_inverted_index.driving | 55 |
| abstract_inverted_index.enhance | 38 |
| abstract_inverted_index.further | 37 |
| abstract_inverted_index.network | 33, 79 |
| abstract_inverted_index.panels, | 127 |
| abstract_inverted_index.present | 68 |
| abstract_inverted_index.problem | 74 |
| abstract_inverted_index.storage | 132 |
| abstract_inverted_index.to/from | 99 |
| abstract_inverted_index.However, | 46 |
| abstract_inverted_index.distance | 59 |
| abstract_inverted_index.electric | 23, 61, 84, 100, 138 |
| abstract_inverted_index.expected | 156 |
| abstract_inverted_index.multiple | 4 |
| abstract_inverted_index.network, | 123 |
| abstract_inverted_index.planned. | 141 |
| abstract_inverted_index.planning | 73, 98 |
| abstract_inverted_index.provides | 148 |
| abstract_inverted_index.realized | 163 |
| abstract_inverted_index.schedule | 117 |
| abstract_inverted_index.strategy | 152 |
| abstract_inverted_index.tolerate | 96 |
| abstract_inverted_index.utilized | 17 |
| abstract_inverted_index.vehicles | 24, 62, 85, 101 |
| abstract_inverted_index.Involving | 19 |
| abstract_inverted_index.consumers | 5 |
| abstract_inverted_index.effective | 150 |
| abstract_inverted_index.generated | 13 |
| abstract_inverted_index.including | 80 |
| abstract_inverted_index.operation | 9, 44, 116, 151, 160 |
| abstract_inverted_index.recourses | 103 |
| abstract_inverted_index.reduction | 40 |
| abstract_inverted_index.scenarios | 106 |
| abstract_inverted_index.vehicles, | 139 |
| abstract_inverted_index.consisting | 124 |
| abstract_inverted_index.formulated | 87 |
| abstract_inverted_index.generation | 21 |
| abstract_inverted_index.scheduling | 147 |
| abstract_inverted_index.stochastic | 71, 105, 115, 146 |
| abstract_inverted_index.conditions. | 109 |
| abstract_inverted_index.interchange | 2, 32, 78, 122 |
| abstract_inverted_index.operational | 72 |
| abstract_inverted_index.possibility | 35 |
| abstract_inverted_index.residential | 120 |
| abstract_inverted_index.contribution | 93 |
| abstract_inverted_index.effectively. | 18 |
| abstract_inverted_index.photovoltaic | 20, 51, 81 |
| abstract_inverted_index.programming. | 91 |
| abstract_inverted_index.uncertainty. | 65 |
| abstract_inverted_index.mixed-integer | 89 |
| abstract_inverted_index.photovoltaics | 126 |
| abstract_inverted_index.water-heating | 130 |
| abstract_inverted_index.large-capacity | 27 |
| abstract_inverted_index.charging/discharging | 97 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.7400000095367432 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.06097768 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |