Integrating Small-Scale Renewable Energy Producers into System-Level Forecasting Processes

Description

As distributed energy resources continue to expand across modern electricity networks, small scale renewable energy producers including community solar farms, microgrids, campus based systems and embedded networks are playing an increasingly important role in system reliability and energy supply. Despite this growing contribution, system level forecasting frameworks remain primarily designed for large scale generators with access to SCADA quality telemetry, continuous monitoring and comprehensive operational datasets.

This project investigates how established forecasting methodologies used by national electricity system operators can be applied by small scale energy producers. Rather than modifying or simplifying these methodologies, the research focuses on identifying the technical, data and operational requirements needed for small producers to align with, provide inputs to and benefit from existing system wide forecasting processes. By enabling stronger alignment between small scale generation and system level forecasting, the project aims to support improved grid visibility, more effective integration of distributed energy resources and enhanced participation of small producers in future energy markets.

Benefits

A scholarship valued at $2,500 is available for a 6 to 8 week engagement on the project.

Eligibility

The project is suitable for a high-performing undergraduate or Master’s level student with a strong background in data science and machine learning. Applicants are expected to have demonstrated proficiency in Python programming, experience working with databases, and a solid understanding of statistical methods relevant to data analysis.