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.

翻译：车辆到建筑（V2B）系统将智能建筑、连接至建筑充电桩的电动汽车（EV）等物理基础设施与数字控制机制相结合，以管理能源使用。通过将电动汽车用作灵活的能量存储单元，建筑可在时变电价和需量电费政策下，动态地对车辆进行充放电，从而优化能源使用并降低成本。这一架构引出了V2B优化问题，即建筑需协调电动汽车的充放电，在满足用户充电需求的同时最小化总用电成本。然而，V2B优化问题具有以下挑战性：（1）波动的电价结构，包括能量电费（$/kWh）和需量电费（$/kW）；（2）较长的规划周期（通常超过30天）；（3）异质性充电桩，其充电速率、可控性和方向性（即单向或双向）各不相同；（4）用户离场时的特定电池电量要求，以确保满足用户需求。与现有方法常将此场景建模为单次组合优化问题不同，我们指出先前工作的关键局限性，并将V2B优化问题建模为马尔可夫决策过程（MDP），即一种随机控制过程。由于状态空间和动作空间庞大，求解所得MDP具有挑战性。为应对大规模状态空间的挑战，我们采用在线搜索方法；针对动作空间，我们利用领域特定的启发式规则来剪枝无前景的动作。我们与日产先进技术中心-硅谷合作验证了所提方法。基于其电动汽车测试平台的数据，我们证明所提出的框架显著优于现有先进方法。