A microgrid energy management system based on chance-constrained stochastic optimization and big data analytics

A Microgrid (MG) is a promising distributed technology to solve todays energy challenges. They are changing how electricity is produced, transmitted, and distributed, enabling to capture massive amounts of data from sensors, and other electrical infrastructures. However, recent advances in modeling and optimization of MG neither integrate the use of big data technologies aggressively nor focus on developing an optimal operational strategy for a single building. To bridge this gap, this research proposes to use Apache Spark to enhance the performance of a scalable stochastic optimization model for an MG for multiple buildings, and to ensure that a significant portion of the wind power output will be utilized. The decision model is formulated as a chance constraint two-stage optimization problem to obtain operation decisions for a behind-the-meter topology. The comparison between the current practice of using historical data and integrating Apache Spark technologies demonstrates the superiority of the streaming data as energy management strategy. Experiments under different settings show that using big data strategy, the model can (1) achieve more cost savings of the total system, (2) increase resiliency to power disturbances, and (3) build a data analytics framework to enhance the decision-making process.