Sustainability and Environmental Performance

Demand response optimization for smart grid integrated buildings: Review of technology enablers landscape and innovation challenges

Liana Toderean, Tudor Cioara, Ionut Anghel, Elissaios Sarmas, Vasilis Michalakopoulos, Vangelis Marinakis

Published 2025

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Abstract

This paper provides a comprehensive overview and analysis of state-of-the-art technological advancements in building integration insmartgrids, with a focus on enabling their participation in demand response (DR). We consolidate knowledge from high-quality sources on the main research topics, helping researchers, building owners, and energy stakeholders to stay informed about the latest developments, trends, and best practices inthe field.Our review covers reputable journals papers that offer technological enablers and evidence-based insights onbuilding interoperability, AI-based energy prediction models, demand optimization and coordination, data privacy, and decentralization.Managing buildings in DR requires careful coordination and control,thuswe provide valuable insights into current practices and opportunities by examining the EU innovation projects and identifying technological innovation trends that aim to increase resident engagement by addressing regulatory and socio-economic concerns. We also discuss the main barriers to buildings’ participation in DR identifying future research directions in the field and providing mitigation insights to the building owners and grid operators. Our findings indicate that despite their potentialbuildingparticipation is limited due to the absence of a clear regulatory framework and lack of mature technologiesto fully support and automate theprogramsimplementation. While AI and optimization technologiesshow promise for improving demand coordination, challenges such as limited interoperability between buildings and energy grids, privacy concerns, and insufficient financial incentivization significantly limit the building’s participation in DR.

Topics

AI building automationAI for Energy Performance OptimizationReview PaperSmart Building Systems and IoT IntegrationSustainable Buildingssmart grids

Cite this paper

@article{d40750a8,
title = {Demand response optimization for smart grid integrated buildings: Review of technology enablers landscape and innovation challenges},
journal = {Energy and Buildings},
volume = {326},
pages = {115067},
year = {2025},
issn = {0378-7788},
doi = {https://doi.org/10.1016/j.enbuild.2024.115067},
url = {https://www.sciencedirect.com/science/article/pii/S0378778824011836},
author = {Liana Toderean and Tudor Cioara and Ionut Anghel and Elissaios Sarmas and Vasilis Michalakopoulos and Vangelis Marinakis},
abstract = {This paper provides a comprehensive overview and analysis of state-of-the-art technological advancements in building integration insmartgrids, with a focus on enabling their participation in demand response (DR). We consolidate knowledge from high-quality sources on the main research topics, helping researchers, building owners, and energy stakeholders to stay informed about the latest developments, trends, and best practices inthe field.Our review covers reputable journals papers that offer technological enablers and evidence-based insights onbuilding interoperability, AI-based energy prediction models, demand optimization and coordination, data privacy, and decentralization.Managing buildings in DR requires careful coordination and control,thuswe provide valuable insights into current practices and opportunities by examining the EU innovation projects and identifying technological innovation trends that aim to increase resident engagement by addressing regulatory and socio-economic concerns. We also discuss the main barriers to buildings’ participation in DR identifying future research directions in the field and providing mitigation insights to the building owners and grid operators. Our findings indicate that despite their potentialbuildingparticipation is limited due to the absence of a clear regulatory framework and lack of mature technologiesto fully support and automate theprogramsimplementation. While AI and optimization technologiesshow promise for improving demand coordination, challenges such as limited interoperability between buildings and energy grids, privacy concerns, and insufficient financial incentivization significantly limit the building’s participation in DR.}
}