Efficient Energy Management for Smart Homes with Electric Vehicles Using Scenario-Based Model Predictive Control Article Swipe
Xinchen Deng
,
Zhongwei Deng
,
Huanhuan Bao
,
Zhiwei Zhao
,
Xiaojia Su
,
Yao Huang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17177678
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/su17177678
Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/su17177678
- https://www.mdpi.com/2071-1050/17/17/7678/pdf?version=1756203148
- OA Status
- gold
- Cited By
- 1
- References
- 24
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413684380
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413684380Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/su17177678Digital Object Identifier
- Title
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Efficient Energy Management for Smart Homes with Electric Vehicles Using Scenario-Based Model Predictive ControlWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-08-26Full publication date if available
- Authors
-
Xinchen Deng, Zhongwei Deng, Huanhuan Bao, Zhiwei Zhao, Xiaojia Su, Yao HuangList of authors in order
- Landing page
-
https://doi.org/10.3390/su17177678Publisher landing page
- PDF URL
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https://www.mdpi.com/2071-1050/17/17/7678/pdf?version=1756203148Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2071-1050/17/17/7678/pdf?version=1756203148Direct OA link when available
- Concepts
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Model predictive control, Energy management, Control (management), Predictive analytics, Computer science, Automotive engineering, Energy (signal processing), Engineering, Artificial intelligence, Machine learning, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4382405499, https://openalex.org/W4404668370, https://openalex.org/W4281612901, https://openalex.org/W1560425127, https://openalex.org/W2765395770, https://openalex.org/W2527754814, https://openalex.org/W2769372420, https://openalex.org/W2013774951, https://openalex.org/W2557055507, https://openalex.org/W2996873326, https://openalex.org/W2914318524, https://openalex.org/W2319491739, https://openalex.org/W3027120897, https://openalex.org/W2783789685, https://openalex.org/W3036333743, https://openalex.org/W3107083634, https://openalex.org/W2216167945, https://openalex.org/W3201072786, https://openalex.org/W4292520129, https://openalex.org/W1970233907, https://openalex.org/W1991024521, https://openalex.org/W2389558405, https://openalex.org/W4304997620, https://openalex.org/W4297825594 |
| referenced_works_count | 24 |
| abstract_inverted_index.A | 146 |
| abstract_inverted_index.a | 5 |
| abstract_inverted_index.PV | 36 |
| abstract_inverted_index.To | 86 |
| abstract_inverted_index.be | 175 |
| abstract_inverted_index.by | 55, 178 |
| abstract_inverted_index.in | 14, 80, 156 |
| abstract_inverted_index.is | 4, 71, 149 |
| abstract_inverted_index.of | 52, 61, 69, 103, 126, 135, 191, 221 |
| abstract_inverted_index.or | 205 |
| abstract_inverted_index.to | 43, 74, 107, 159, 174, 195 |
| abstract_inverted_index.MPC | 47, 112, 163, 203, 214 |
| abstract_inverted_index.The | 183 |
| abstract_inverted_index.and | 25, 38, 90, 122 |
| abstract_inverted_index.are | 129, 140 |
| abstract_inverted_index.can | 41, 48, 215 |
| abstract_inverted_index.for | 10 |
| abstract_inverted_index.net | 136 |
| abstract_inverted_index.the | 50, 57, 67, 76, 98, 110, 119, 132, 152, 161, 170, 189, 209, 218 |
| abstract_inverted_index.via | 180 |
| abstract_inverted_index.(PV) | 20 |
| abstract_inverted_index.This | 168 |
| abstract_inverted_index.into | 165 |
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| abstract_inverted_index.load | 39, 137 |
| abstract_inverted_index.more | 196 |
| abstract_inverted_index.some | 83 |
| abstract_inverted_index.that | 17, 187, 212 |
| abstract_inverted_index.this | 94 |
| abstract_inverted_index.turn | 81 |
| abstract_inverted_index.used | 7 |
| abstract_inverted_index.with | 35, 201, 204 |
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| abstract_inverted_index.Carlo | 144 |
| abstract_inverted_index.Model | 0 |
| abstract_inverted_index.Monte | 143 |
| abstract_inverted_index.Then, | 131 |
| abstract_inverted_index.about | 151 |
| abstract_inverted_index.error | 139 |
| abstract_inverted_index.first | 153 |
| abstract_inverted_index.homes | 16, 128 |
| abstract_inverted_index.leads | 194 |
| abstract_inverted_index.loss. | 85 |
| abstract_inverted_index.lower | 75 |
| abstract_inverted_index.often | 72 |
| abstract_inverted_index.paper | 95 |
| abstract_inverted_index.power | 37 |
| abstract_inverted_index.smart | 15, 127 |
| abstract_inverted_index.value | 60 |
| abstract_inverted_index.which | 79 |
| abstract_inverted_index.(EVs), | 24 |
| abstract_inverted_index.C-ADMM | 179 |
| abstract_inverted_index.First, | 118 |
| abstract_inverted_index.action | 155 |
| abstract_inverted_index.allows | 169 |
| abstract_inverted_index.causes | 82 |
| abstract_inverted_index.demand | 40 |
| abstract_inverted_index.energy | 27 |
| abstract_inverted_index.error, | 208 |
| abstract_inverted_index.error. | 223 |
| abstract_inverted_index.errors | 33, 54 |
| abstract_inverted_index.impact | 51, 220 |
| abstract_inverted_index.layout | 121 |
| abstract_inverted_index.method | 102 |
| abstract_inverted_index.models | 125 |
| abstract_inverted_index.number | 68, 190 |
| abstract_inverted_index.online | 8, 88 |
| abstract_inverted_index.solved | 177 |
| abstract_inverted_index.system | 120 |
| abstract_inverted_index.verify | 186 |
| abstract_inverted_index.achieve | 87 |
| abstract_inverted_index.address | 217 |
| abstract_inverted_index.battery | 26 |
| abstract_inverted_index.burden, | 78 |
| abstract_inverted_index.control | 2, 154 |
| abstract_inverted_index.losses. | 45 |
| abstract_inverted_index.panels, | 21 |
| abstract_inverted_index.problem | 113, 164, 173 |
| abstract_inverted_index.quickly | 108, 176 |
| abstract_inverted_index.results | 185, 210 |
| abstract_inverted_index.storage | 28 |
| abstract_inverted_index.systems | 29 |
| abstract_inverted_index.through | 142 |
| abstract_inverted_index.without | 114, 206 |
| abstract_inverted_index.(BESSs). | 30 |
| abstract_inverted_index.(C-ADMM) | 105 |
| abstract_inverted_index.However, | 31, 65 |
| abstract_inverted_index.benefits | 13 |
| abstract_inverted_index.commonly | 6 |
| abstract_inverted_index.compared | 200 |
| abstract_inverted_index.designed | 150 |
| abstract_inverted_index.economic | 12, 44, 84, 92, 197, 219 |
| abstract_inverted_index.electric | 22 |
| abstract_inverted_index.expected | 58 |
| abstract_inverted_index.maximize | 91 |
| abstract_inverted_index.mitigate | 49 |
| abstract_inverted_index.multiple | 62, 166 |
| abstract_inverted_index.original | 171 |
| abstract_inverted_index.parallel | 181 |
| abstract_inverted_index.proposes | 96 |
| abstract_inverted_index.reducing | 66, 115 |
| abstract_inverted_index.relevant | 123, 184 |
| abstract_inverted_index.strategy | 9 |
| abstract_inverted_index.vehicles | 23 |
| abstract_inverted_index.algorithm | 106 |
| abstract_inverted_index.benefits, | 93 |
| abstract_inverted_index.benefits. | 198 |
| abstract_inverted_index.calculate | 109 |
| abstract_inverted_index.component | 124 |
| abstract_inverted_index.computing | 56 |
| abstract_inverted_index.consensus | 99, 147 |
| abstract_inverted_index.decompose | 160 |
| abstract_inverted_index.different | 157 |
| abstract_inverted_index.direction | 101 |
| abstract_inverted_index.generated | 141 |
| abstract_inverted_index.integrate | 18 |
| abstract_inverted_index.necessary | 73 |
| abstract_inverted_index.objective | 59 |
| abstract_inverted_index.operation | 89 |
| abstract_inverted_index.scenarios | 70, 134, 158, 193 |
| abstract_inverted_index.utilizing | 97 |
| abstract_inverted_index.associated | 34 |
| abstract_inverted_index.computing. | 182 |
| abstract_inverted_index.constraint | 148 |
| abstract_inverted_index.increasing | 188 |
| abstract_inverted_index.maximizing | 11 |
| abstract_inverted_index.prediction | 32, 53, 138, 207, 222 |
| abstract_inverted_index.predictive | 1 |
| abstract_inverted_index.scenarios. | 64, 117 |
| abstract_inverted_index.stochastic | 63, 116, 133, 192 |
| abstract_inverted_index.alternating | 100 |
| abstract_inverted_index.computation | 77 |
| abstract_inverted_index.demonstrate | 211 |
| abstract_inverted_index.effectively | 216 |
| abstract_inverted_index.large-scale | 172 |
| abstract_inverted_index.multipliers | 104 |
| abstract_inverted_index.simulation. | 145 |
| abstract_inverted_index.traditional | 202 |
| abstract_inverted_index.Furthermore, | 199 |
| abstract_inverted_index.established. | 130 |
| abstract_inverted_index.photovoltaic | 19 |
| abstract_inverted_index.sub-problems. | 167 |
| abstract_inverted_index.Scenario-based | 46 |
| abstract_inverted_index.scenario-based | 111, 162, 213 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5000590649 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 6 |
| corresponding_institution_ids | https://openalex.org/I4210131649 |
| citation_normalized_percentile.value | 0.84274786 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |