To realize the goals of energy transition, becoming energy-neutral at the neighborhood level by sharing energy among clusters of heterogeneous buildings with local distributed energy resources (DERs), will play a vital role. However, uncertainties related to demand and renewable sources pose a major operational challenge to schedule the DERs. In this paper, a scenario-based mixed-integer linear programming (MILP) model is proposed for an energy management system (EMS) of a local energy community. The proposed EMS executes a stochastic day-ahead scheduling operation of multi-energy systems (MES). A set of scenarios are generated with the Gaussian mixture model (GMM) to consider uncertainties of demand and renewable sources. Moreover, Monte Carlo simulations (MCS) are performed to assess the effectiveness of the proposed EMS compared to the deterministic one. The proposed method is validated by using a real-world case study of a generic Dutch university medical campus in Amsterdam, the Netherlands. Two types of analysis are performed: one-day analysis and seasonal analysis. In both cases, in an average, the stochastic process outperforms the deterministic process considerably, in terms of cost, ${\mathit{CO}}_2$ emission, imported electricity from grid and usage of local energy resources.

为了实现能源过渡的目标，通过在具有本地分布式能源（DER）的异构建筑群之间共享能源，在邻居级别实现能源中立将发挥至关重要的作用。但是，与需求和可再生资源有关的不确定性对调度DER提出了重大的运营挑战。在本文中，为本地能源社区的能源管理系统（EMS）提出了基于场景的混合整数线性规划（MILP）模型。拟议的EMS执行多能源系统（MES）的随机日前调度操作。使用高斯混合模型（GMM）生成了一组方案，以考虑需求和可再生资源的不确定性。此外，与确定性方法相比，执行了蒙特卡洛模拟（MCS）以评估提议的EMS的有效性。通过使用在荷兰阿姆斯特丹的一家通用荷兰大学医学校园的实际案例研究验证了所提出的方法。进行两种类型的分析：一日分析和季节性分析。在这两种情况下，就成本而言，平均而言，随机过程要比确定性过程好得多，${\mathit{一氧化碳}}_2$ 排放，电网的进口电力以及当地能源的使用。