Microgrids play an essential role in the integration of multiple distributed energy resources in buildings. They can meet critical loads in buildings while reducing peak loads and congestion and providing other types of ancillary services to the main electrical grid. Sizing the components of microgrids and scheduling their optimal operation while jointly integrating uncertain renewable energy generation and loads can significantly affect its ability to meet these objectives. In fact, microgrids face great challenges due to renewable energies and load uncertainties. In this paper, a two-stage stochastic programming model for optimal sizing and operation of various distributed energy resources for peak load shaving in district buildings is proposed. The first stage is related to the planning of photovoltaic panels and batteries, while the second stage aims to find the optimal operation of the system in grid-connected mode. The uncertainties are related to photovoltaic power generation and loads. The Generative Adversarial Network (GAN) is implemented to generate the scenarios of uncertain parameters, and the k-medoids classical method for scenario reduction is applied to decrease the number of scenarios. The proposed two-stage stochastic optimization is tested for a real case study of a university campus in Canada.

微电网在建筑物中多种分布式能源的整合中发挥着至关重要的作用。它们可以满足建筑物中的关键负载，同时减少峰值负载和拥堵，并为主电网提供其他类型的辅助服务。在联合整合不确定的可再生能源发电和负载的同时，确定微电网组件的大小并安排其最佳运行，会显着影响其实现这些目标的能力。事实上，由于可再生能源和负载的不确定性，微电网面临着巨大的挑战。在本文中，提出了一种两阶段随机规划模型，用于优化各种分布式能源的规模和运行，以在区域建筑中进行调峰。第一阶段涉及光伏板和电池的规划，第二阶段旨在寻找系统在并网模式下的最优运行。不确定性与光伏发电和负荷有关。生成对抗网络（GAN）用于生成不确定参数的场景，并应用 k-medoids 经典的场景缩减方法来减少场景数量。针对加拿大一所大学校园的真实案例研究对所提出的两阶段随机优化进行了测试。