Vehicle-to-Building (V2B) Charging Optimization

Overview

Developing intelligent systems for Vehicle-to-Building (V2B) charging that optimize energy usage, reduce costs, and improve grid stability through advanced algorithms and machine learning techniques.

Key Projects

OPTIMUS: Discrete Event Simulator

A comprehensive discrete event simulator for optimizing V2B charging operations, enabling detailed analysis of charging strategies and their impact on building energy systems.

Publication: OPTIMUS: Discrete Event Simulator for Vehicle-to-Building Charging Optimization, IEEE SmartComp 2024

CONSENT: Negotiation Framework

An innovative negotiation framework that leverages user flexibility to optimize V2B charging under uncertainty, balancing user preferences with grid constraints.

Status: Under review (2026)

Reinforcement Learning for V2B

Developed reinforcement learning approaches for heterogeneous agents with long-term reward optimization in V2B charging scenarios.

Status: Preprint (2025)

Online Decision-Making Under Uncertainty

Created online decision-making algorithms that adapt to real-time uncertainty in V2B systems, improving reliability and efficiency.

Status: Preprint (2025)

Technologies

• Mixed Integer Linear Programming (MILP)
• Reinforcement Learning
• Stochastic Optimization
• Discrete Event Simulation
• Python, MATLAB

Impact

These systems enable more efficient integration of electric vehicles into building energy management, reducing costs and environmental impact while maintaining grid stability.