Energy sharing within building complexes depends greatly on the complex load adaptability and policy support. However, most building complex microgrid research concentrates on configuration and operation optimization, rather than energy contributions from different users. The objective of this study is to analyze energy sharing ability and load adaptability for a building complex comprising multiple building types, under transaction liberalization restrictions in different energy markets. The specific research for this study is a smart community in Kitakyushu, Japan, with 49 buildings of different types. The complex's microgrid system in this study comprises photovoltaic, battery, and vehicle to grid services. We obtained six representative typical clusters by reducing the 49 buildings’ dimensionality using principal components and clustering, then Monte Carlo simulation to predict permissible discharge capacities for vehicle to grid (V2G) services. We propose three demand-side liberalization scenarios: self-use, photovoltaic feed-in tariffs (FITs), and free trade; optimizing system configuration under different scenarios using a genetic algorithm. Results confirm that shopping malls achieve best economic benefits with self-use scenario; whereas office building adaptability is more significant for the other scenarios. These results will contribute to user selection and system design for building complex microgrid systems.

建筑群内的能源共享在很大程度上取决于复杂的负荷适应性和政策支持。然而，大多数建筑复杂微电网研究集中在配置和运行优化上，而不是来自不同用户的能源贡献。本研究的目的是分析在不同能源市场的交易自由化限制下，由多种建筑类型组成的建筑群的能源共享能力和负荷适应性。本研究的具体研究对象是日本北九州的一个智慧社区，拥有 49 座不同类型的建筑。本研究中该综合体的微电网系统包括光伏、电池和车辆到电网服务。我们通过使用主成分和聚类对 49 个建筑物的维度进行降维，得到了 6 个具有代表性的典型聚类，然后进行蒙特卡罗模拟来预测车辆到电网 (V2G) 服务的允许放电容量。我们提出了三种需求侧自由化情景：自用、光伏上网电价（FITs）和自由贸易；使用遗传算法优化不同场景下的系统配置。结果证实，商场自用场景经济效益最佳；而对于其他场景，写字楼的适应性更为显着。这些结果将有助于构建复杂微电网系统的用户选择和系统设计。使用遗传算法优化不同场景下的系统配置。结果证实，商场自用场景经济效益最佳；而对于其他场景，写字楼的适应性更为显着。这些结果将有助于构建复杂微电网系统的用户选择和系统设计。使用遗传算法优化不同场景下的系统配置。结果证实，商场自用场景经济效益最佳；而对于其他场景，写字楼的适应性更为显着。这些结果将有助于构建复杂微电网系统的用户选择和系统设计。