V2B（Vehicle-to-Building）是V2X（Vehicle-to-Everything）技术的一个经济提议。有许多研究证明，V2B技术可以有效地提高建筑系统的能源使用效率。然而，当前大部分关于V2B有效性或应用性的研究中，都默认有双向充电功能的电动汽车，愿意配合建筑进行反向充电以促进建筑的能源调整活动。同时很少有研究论证，电网激励可能会通过影响建筑行为间接鼓励电动车（EVs）参与V2B活动。在本文中，使用Stackelberg博弈模型来描述建筑和电动车之间的两阶段博弈，假设电动车和建筑都旨在通过绿色活动最大化利润或最小化成本。模型的建立考虑到了电动汽车的不满意成本，并且采用Stevens‘ law方法来量化电动车的响应率。同时文章使用了分段线性化、大M法和KKT约束等方法，将V2B-GM模型重新构建为混合整数线性规划（MILP）问题。案例分析结果显示，所提出的激励政策可以有效促使建筑和电动车参与低碳互动，有助于电网的削峰。敏感度分析等一系列仿真结果，对模型的应用提供了基础。此外，本文分析了描述电动汽车行为的数据特征，并基于这些特征以及数据之间的关系，使用蒙特卡罗方法和核密度估计方法进一步生成更大规模案例，以分析大规模下电动汽车、光伏产能对系统运行的影响。最后，文章进一步考虑了电网提供的两部制电价策略，结果表明，采用两部制电价策略可以进一步激励电动汽车参与V2B，提高系统内的能量转移效率。本文为V2B技术在促进建筑能效和电网管理中的实际应用提供了理论依据和政策建议。
V2B (Vehicle-to-Building) is an economic proposition within the V2X (Vehicle-to-Everything) technology. Numerous studies have demonstrated that V2B technology can effectively enhance the energy utilization efficiency of building systems. However, most current research on the effectiveness or applicability of V2B assumes that electric vehicles (EVs) with bidirectional charging capabilities are willing to participate in reverse charging to facilitate energy adjustment activities in buildings. At the same time, few studies have argued that grid incentives might indirectly encourage EVs to engage in V2B activities by influencing building behaviors. In this thesis, the Stackelberg game model is used to describe the two-stage game between buildings and EVs, assuming that both EVs and buildings aim to maximize profits or minimize costs through green activities. The model takes into account the dissatisfaction costs of EVs and employs the Stevens‘ Law method to quantify the responsiveness of EVs. Additionally, the thesis uses piecewise linearization, the Big-M method, and KKT constraints to reformulate the V2B-GM model as a Mixed-Integer Linear Programming (MILP) problem.This thesis examines the efficacy of incentive policies to facilitate low-carbon interactions between buildings and electric vehicles (EVs), contributing to grid peak shaving. Utilizing a series of simulations, including sensitivity analysis, the foundational applicability of the model is established. We analyze the behavior of EVs using data characteristics and employ Monte Carlo methods and kernel density estimation to generate larger-scale scenarios, thereby assessing the impact of significant EV and photovoltaic capacities on system operations. Considering a two-part tariff strategy proposed by the grid, our findings indicate that such pricing mechanisms can significantly encourage EV participation in Vehicle-to-Building (V2B) interactions, thereby enhancing energy transfer efficiency within the system. This research underscores the potential of targeted incentives in promoting sustainable energy practices in urban infrastructures.This thesis provides further guidance for the low-carbon strategies of buildings and offers effective suggestions for incentives from the grid to buildings and then to EVs.
