Online Decision-Making Under Uncertainty for Vehicle-to-Building Systems

2025-05-06

International Conference on Cyber-Physical Systems

AI Summary

This study addresses the challenge of optimizing electricity costs in vehicle-to-building (V2B) systems under multiple uncertainties, including dynamic electricity pricing, long-horizon scheduling, heterogeneous charging infrastructure, and stochastic user demands. To overcome the limitations of conventional single-stage combinatorial optimization approaches, this work is the first to formally model the problem as a stochastic Markov decision process. The authors propose a novel policy that integrates online search with domain-informed action pruning to effectively manage the high-dimensional state and action spaces inherent in real-world V2B operations. Evaluated on real-world electric vehicle data from the Nissan Silicon Valley Advanced Technology Center, the proposed method demonstrates significant performance improvements over current state-of-the-art solutions.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Reasoning under Uncertainty: Stochastic Optimization
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems

Abstract

Vehicle-to-building (V2B) systems integrate physical infrastructures, such as smart buildings and electric vehicles (EVs) connected to chargers at the building, with digital control mechanisms to manage energy use. By utilizing EVs as flexible energy reservoirs, buildings can dynamically charge and discharge them to optimize energy use and cut costs under time-variable pricing and demand charge policies. This setup leads to the V2B optimization problem, where buildings coordinate EV charging and discharging to minimize total electricity costs while meeting users' charging requirements. However, the V2B optimization problem is challenging because of: (1) fluctuating electricity pricing, which includes both energy charges ($/kWh) and demand charges ($/kW); (2) long planning horizons (typically over 30 days); (3) heterogeneous chargers with varying charging rates, controllability, and directionality (i.e., unidirectional or bidirectional); and (4) user-specific battery levels at departure to ensure user requirements are met. In contrast to existing approaches that often model this setting as a single-shot combinatorial optimization problem, we highlight critical limitations in prior work and instead model the V2B optimization problem as a Markov decision process (MDP), i.e., a stochastic control process. Solving the resulting MDP is challenging due to the large state and action spaces. To address the challenges of the large state space, we leverage online search, and we counter the action space by using domain-specific heuristics to prune unpromising actions. We validate our approach in collaboration with Nissan Advanced Technology Center - Silicon Valley. Using data from their EV testbed, we show that the proposed framework significantly outperforms state-of-the-art methods.

Problem

• Vehicle-to-Building
• online decision-making
• uncertainty
• energy cost optimization
• electric vehicles

Innovation

• Markov decision process
• online search
• domain-specific heuristics
• vehicle-to-building
• stochastic control