Adaptive Robust Linear Programming Model for the Charging Scheduling and Reactive Power Control of EV Fleets

Author(s)

Arias, Nataly Banol ; Lopez, Juan C. ; Rider, Marcos J. ; Franco, John Fredy ; IEEE

• Document type: Journal article
• Source: 2021 IEEE MADRID POWERTECH; v. N/A, p. 6-pg., 2021-01-01.

Abstract

High penetration of electric vehicles (EVs) triggers challenges and opportunities for distribution system operators. Inverter-based EV chargers with active/reactive power control can be used to coordinate the EV fleet's charging process while providing local volt/var regulation. This paper proposes an adaptive robust programming model for the charging scheduling of EV fleets that exploits their capability to locally support the grid via reactive power control. The proposed model aims at maximizing the aggregator's revenue while considering the worst-case scenario in terms of active power losses at the supporting grid. Operational constraints of unbalanced three-phase distribution networks under demand uncertainty are also enforced. The proposed robust model is a min-max problem that can be linearized and solved using a column-and-constraint generation (C&CG) method. Tests performed in a 25-node distribution system illustrate the EV fleet's capacity to support the grid while minimizing the total energy not supplied. (AU)

• FAPESP's process: 19/01906-0 - Optimal distributed restoration of electrical distribution systems using alternating direction method of multipliers
• Grantee: Juan Camilo Lopez Amezquita
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 18/23617-7 - Optimization of modern distribution system operation via Electric Vehicles' flexibility and stationary batteries integrated into three-phase unbalanced networks
• Grantee: Maria Nataly Banol Arias
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 15/21972-6 - Optimization of the operation and planning in transmission and distribution systems
• Grantee: Rubén Augusto Romero Lázaro
• Support Opportunities: Research Projects - Thematic Grants