March 18, 2022
Conference Paper

Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems

Abstract

Marine renewable energy (MRE) resources are highly predictable and persistent sources of energy, when compared to other renewable sources like wind and solar. These lend them favorably for potential grid applications, particularly for coastal/island power systems where their generation potential is high. Island power systems, on the other hand, are either supported by onsite generation or by transported energy from the mainland grid. Therefore, robustness of grid operations depend heavily on the diversity of onsite generation resources and the reliability of the power transportation medium. Issues relating to either of these two factors may lead to impediments in smooth and reliable operation of the power system. Analyzing and quantifying operational risks for such island power systems with diverse non-conventional generation portfolios through conventional techniques can also prove to be cumbersome, often requiring multiple different inputs. Therefore, in this paper, we firstly present a novel, purely data-driven formulation which quantifies the operational reliability of such island power systems through minimal input data. Specifically, our proposed methodology only relies on historical knowledge of typical hourly load and generation profiles to quantify associated operational risks. Subsequently, we use our proposed formulation to evaluate the effectiveness of MRE resources (over other renewable resources like wind and solar) in providing resilience benefits to island power systems. The proposed formulation is demonstrated with a case study for an island power system in Nantucket, MA.

Published: March 18, 2022

Citation

Chalishazar V.H., S. Bhattacharya, S. Hanif, D. Bhatnagar, M.E. Alam, B. Robertson, and D.C. Preziuso, et al. 2021. Data-Driven Reliability Assessment for Marine Renewable Energy Enabled Island Power Systems. In IEEE Power & Energy Society General Meeting (PESGM 2021), July 26-29, 2021, Washington DC, 1-5. Piscataway, New Jersey:IEEE. PNNL-SA-157769. doi:10.1109/PESGM46819.2021.9637995