Stochastic Scenario-Based Multi-Objective MILP Optimization of Large-Scale EV Fleets in V2G-Enabled Smart Grids Considering Battery Degradation and Lifecycle Emissions.
Saved in:
| Title: | Stochastic Scenario-Based Multi-Objective MILP Optimization of Large-Scale EV Fleets in V2G-Enabled Smart Grids Considering Battery Degradation and Lifecycle Emissions. |
|---|---|
| Authors: | Gül, Ozan1 (AUTHOR) ogul@bingol.edu.tr, Kökçam, Ebubekir1 (AUTHOR) |
| Source: | Energies (19961073). May2026, Vol. 19 Issue 10, p2398. 26p. |
| Subject Terms: | *Electric vehicles, *Mixed integer linear programming, *Smart power grids, *Energy storage, *Carbon emissions, *Clean energy, *Stochastic programming |
| Abstract: | The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO2eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: enr DbLabel: Energy & Power Source An: 194141513 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Stochastic Scenario-Based Multi-Objective MILP Optimization of Large-Scale EV Fleets in V2G-Enabled Smart Grids Considering Battery Degradation and Lifecycle Emissions. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gül%2C+Ozan%22">Gül, Ozan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ogul@bingol.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Kökçam%2C+Ebubekir%22">Kökçam, Ebubekir</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. May2026, Vol. 19 Issue 10, p2398. 26p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Electric+vehicles%22">Electric vehicles</searchLink><br />*<searchLink fieldCode="DE" term="%22Mixed+integer+linear+programming%22">Mixed integer linear programming</searchLink><br />*<searchLink fieldCode="DE" term="%22Smart+power+grids%22">Smart power grids</searchLink><br />*<searchLink fieldCode="DE" term="%22Energy+storage%22">Energy storage</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br />*<searchLink fieldCode="DE" term="%22Clean+energy%22">Clean energy</searchLink><br />*<searchLink fieldCode="DE" term="%22Stochastic+programming%22">Stochastic programming</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The integration of large-scale electric vehicle (EV) fleets into vehicle-to-grid (V2G) systems offers significant potential for enhancing the operation of renewable-based smart grids. However, stochastic uncertainties in photovoltaic (PV) generation, vehicle availability, and load demand—coupled with battery degradation and life-cycle assessment (LCA) carbon emissions—pose major challenges to optimal scheduling. This paper proposes a scenario-based multi-objective MILP framework for a 500-EV fleet aggregator. The model incorporates Monte Carlo simulations for multi-source uncertainty quantification (±25% PV forecast errors, ±40% availability), LCA penalties (45 kgCO2eq/kWh), and ancillary service revenues (25 USD/MW-h). Long-term state-of-health (SOH) projections, including a 1-year fade to 96.5%, are also integrated. Comparative analysis of V2X scenarios shows that the V2G Hybrid strategy reduces daily costs by 34.6% (from ~11,000 USD in the uncontrolled case to 7741 USD when reserve revenues are included), achieves over 50% peak shaving, and maintains voltage stability within 0.994–1.008 pu. The stochastic Pareto frontier identifies knee-point solutions that lower normalized expected costs to 134.61 while achieving 1–2% lower expected emissions compared to deterministic baselines. These results demonstrate a comprehensive framework, uncertainty-aware framework that balances economic viability, grid resilience, and environmental sustainability, offering actionable insights for fleet aggregators and policymakers working toward net-zero energy systems. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194141513 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19102398 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 2398 Subjects: – SubjectFull: Electric vehicles Type: general – SubjectFull: Mixed integer linear programming Type: general – SubjectFull: Smart power grids Type: general – SubjectFull: Energy storage Type: general – SubjectFull: Carbon emissions Type: general – SubjectFull: Clean energy Type: general – SubjectFull: Stochastic programming Type: general Titles: – TitleFull: Stochastic Scenario-Based Multi-Objective MILP Optimization of Large-Scale EV Fleets in V2G-Enabled Smart Grids Considering Battery Degradation and Lifecycle Emissions. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gül, Ozan – PersonEntity: Name: NameFull: Kökçam, Ebubekir IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 10 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
