Data Economy for Prosumers in a Smart Grid Ecosystem

TL;DR

This paper explores a data-driven approach within smart grid ecosystems, focusing on local data models for flexibility and forecasting in home energy management systems to enhance renewable energy integration.

Contribution

It introduces a functional architecture for HEMS and discusses data-driven models for renewable forecasting and flexibility modeling under privacy constraints.

Findings

Improved renewable energy forecasting accuracy

Effective local flexibility modeling

Enhanced privacy-preserving data exchange mechanisms

Abstract

Smart grids technologies are enablers of new business models for domestic consumers with local flexibility (generation, loads, storage) and where access to data is a key requirement in the value stream. However, legislation on personal data privacy and protection imposes the need to develop local models for flexibility modeling and forecasting and exchange models instead of personal data. This paper describes the functional architecture of an home energy management system (HEMS) and its optimization functions. A set of data-driven models, embedded in the HEMS, are discussed for improving renewable energy forecasting skill and modeling multi-period flexibility of distributed energy resources.