Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility
Aggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/8/2947 |
| _version_ | 1797446597190090752 |
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| author | Yuxuan Wang Bingxu Zhang Chenyang Li Yongzhang Huang |
| author_facet | Yuxuan Wang Bingxu Zhang Chenyang Li Yongzhang Huang |
| author_sort | Yuxuan Wang |
| collection | DOAJ |
| description | Aggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In this paper, plugged-in EV is refined into three categories: rated power charging, adjustable charging, and flexible charging–discharging, and then control models are established separately; the concept of temporal flexibility for EV clusters is proposed for the adjustable charging and flexible charging–discharging of EV sets; then, the schedule boundary of EV clusters is determined under the flexibility constraints. The interval is used to describe the intermittent nature of renewable energy, and the minimum operating cost of the system is taken as the goal to construct a distributed energy robust optimization model. By decoupling the model, a two-stage efficient solution is achieved. An example analysis verifies the effectiveness and superiority of the proposed strategy. The proposed strategy can minimize the total cost while meeting the demand difference of EV users. |
| first_indexed | 2024-03-09T13:42:50Z |
| format | Article |
| id | doaj.art-f2fd64c1c7a04fb18654b7a3c2b12726 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:42:50Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-f2fd64c1c7a04fb18654b7a3c2b127262023-11-30T21:04:43ZengMDPI AGEnergies1996-10732022-04-01158294710.3390/en15082947Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load FlexibilityYuxuan Wang0Bingxu Zhang1Chenyang Li2Yongzhang Huang3School of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Electrical & Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaAggregated electric vehicles (EVs) integrated to the grid and intermittent wind and solar energy increased the complexity of the economic dispatch of the power grid. Aggregated EVs have a great potential to reduce system operating costs because of their dual attributes of load and energy storage. In this paper, plugged-in EV is refined into three categories: rated power charging, adjustable charging, and flexible charging–discharging, and then control models are established separately; the concept of temporal flexibility for EV clusters is proposed for the adjustable charging and flexible charging–discharging of EV sets; then, the schedule boundary of EV clusters is determined under the flexibility constraints. The interval is used to describe the intermittent nature of renewable energy, and the minimum operating cost of the system is taken as the goal to construct a distributed energy robust optimization model. By decoupling the model, a two-stage efficient solution is achieved. An example analysis verifies the effectiveness and superiority of the proposed strategy. The proposed strategy can minimize the total cost while meeting the demand difference of EV users.https://www.mdpi.com/1996-1073/15/8/2947EV clusterschedulable capabilitytemporal flexibilitydemand differencerobust optimization |
| spellingShingle | Yuxuan Wang Bingxu Zhang Chenyang Li Yongzhang Huang Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility Energies EV cluster schedulable capability temporal flexibility demand difference robust optimization |
| title | Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility |
| title_full | Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility |
| title_fullStr | Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility |
| title_full_unstemmed | Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility |
| title_short | Collaborative Robust Optimization Strategy of Electric Vehicles and Other Distributed Energy Considering Load Flexibility |
| title_sort | collaborative robust optimization strategy of electric vehicles and other distributed energy considering load flexibility |
| topic | EV cluster schedulable capability temporal flexibility demand difference robust optimization |
| url | https://www.mdpi.com/1996-1073/15/8/2947 |
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