Towards a Holistic Microgrid Performance Framework and a Data-Driven Assessment Analysis

• Main Authors: Apostolos C. Tsolakis, Ilias Kalamaras, Thanasis Vafeiadis, Lampros Zyglakis, Angelina D. Bintoudi, Adamantia Chouliara, Dimosthenis Ioannidis, Dimitrios Tzovaras
• Format: Article
• Language: English
• Published: MDPI AG 2020-11-01
• Series: Energies
• Subjects: microgrid; performance; key performance indicators; t-SNE; dynamic time warping
• Online Access: https://www.mdpi.com/1996-1073/13/21/5780

On becoming a commodity, Microgrids (MGs) have started gaining ground in various sizes (e.g., nanogrids, homegrids, etc.) and forms (e.g., local energy communities) leading an exponential growth in the respective sector. From demanding deployments such as military bases and hospitals, to tertiary and residential buildings and neighborhoods, MG systems exploit renewable and conventional generation assets, combined with various storage capabilities to deliver a completely new set of business opportunities and services in the context of the Smart Grid. As such systems involve economic, environmental and technical aspects, their performance is quite difficult to evaluate, since there are not any standards that cover all of these aspects, especially during operational stages. Towards allowing an holistic definition of a MG performance, for both design and operational stages, this paper first introduces a complete set of Key Performance Indicators to measure holistically the performance of a MG’s life cycle. Following, focusing on the MG’s day-to-day operation, a data-driven assessment is proposed, based on dynamic metrics, custom made reference models, and smart meter data, in order to be able to extract its operational performance. Two different algorithmic implementations (i.e., Dynamic Time Warping and t-distributed Stochastic Neighbor Embedding) are used to support the methodology proposed, while real-life data are used from a small scale MG to provide the desired proof-of-concept. Both algorithms seem to correctly identify days and periods of not optimal operation, hence presenting promising results for MG performance assessment, that could lead to a MG Performance Classification scheme.