Developing energy flexibility in clusters of buildings: A critical analysis of barriers from planning to operation

Energy and Buildings, Journal Year: 2023, Volume and Issue: 300, P. 113608 - 113608

Published: Oct. 5, 2023

This paper examines building energy flexibility at an aggregated level and addresses the main barriers research gaps for development of this resource across three design phases: market policy, early planning design, operation. We review methodologies tools discuss barriers, challenges, opportunities, incorporating economic, technical, professional, social perspectives. Although various legal regulatory frameworks exist to foster small buildings, financing mechanisms are limited with a significant number perceived risks undermining private investment. For phase, planners designers lack appropriate face interoperability which often results in insufficient consideration demand response programs. The operational phase highlighted socio-technical challenges related both complexity deployment communication, as well privacy acceptability issues. Finally, proposes targeted directions address promote greater deployments, including capturing side dynamics, improving baseline estimations developing seamless connectivity between buildings districts.

Language: Английский

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A novel multi-level predictive management strategy to optimize phase-change energy storage and building-integrated renewable technologies operation under dynamic tariffs

Energy Conversion and Management, Journal Year: 2023, Volume and Issue: 291, P. 117220 - 117220

Published: June 12, 2023

Language: Английский

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Energy flexibility quantification of a tropical net-zero office building using physically consistent neural network-based model predictive control

Advances in Applied Energy, Journal Year: 2024, Volume and Issue: 14, P. 100167 - 100167

Published: Feb. 24, 2024

Building energy flexibility plays a critical role in demand-side management for reducing utility costs building owners and sustainable, reliable, smart grids. Realizing tropical regions requires solar photovoltaics storage systems. However, quantifying the of buildings utilizing such technologies has yet to be explored, robust control sequence is needed this scenario. Hence, work presents case study evaluate controls operations net-zero office Singapore. The utilizes data-driven quantification workflow employs novel model predictive (MPC) framework based on physically consistent neural network (PCNN) optimize flexibility. To best our knowledge, first instance that PCNN applied mathematical MPC setting, stability system formally proved. Three scenarios are evaluated compared: default regulated flat tariff, real-time pricing mechanism, an on-site battery (BESS). Our findings indicate incorporating into could more beneficial leverage decisions than flat-rate approach. Moreover, adding BESS PV generation improved self-sufficiency self-consumption by 17% 20%, respectively. This integration also addresses mismatch issues within framework, thus ensuring reliable local supply. Future research can proposed PCNN-MPC different types.

Language: Английский

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Enabling portable demand flexibility control applications in virtual and real buildings

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: 86, P. 108645 - 108645

Published: Jan. 30, 2024

Control applications that facilitate Demand Flexibility (DF) are difficult to deploy at scale in existing buildings. The heterogeneity of systems and non-standard naming conventions for metadata describing data points building automation often lead ad-hoc building-specific applications. In recent years, several researchers investigated semantic models describe the meaning data. They suggest these can enhance deployment applications, enabling exchanges among heterogeneous sources their portability across different However, studies question fail explore capabilities context controls. This paper proposes a novel semantics-driven framework developing deploying portable DF control design leverages an iterative science research methodology, evolving from evidence gathered through simulation field demonstrations. aims decouple specific buildings platforms, be configured semi-automatically. allows application developers streamline onboarding new could otherwise time-consuming resource-intensive. has been validated its capability sharing same codebase diverse virtual real demonstration successfully tested two controls load shifting shedding four using Building Optimization Testing Framework (BOPTEST) one platform VOLTTRON. Insights into current limitations, benefits, challenges generalizable derived efforts outcomes guide future this field.

Language: Английский

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Scientometric mapping of global research on the environmental performance of existing buildings: Advancements, challenges and prospects

Journal of Cleaner Production, Journal Year: 2025, Volume and Issue: unknown, P. 144774 - 144774

Published: Jan. 1, 2025

Language: Английский

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Optimizing energy flexibility through electricity price-responsiveness and thermal load management in buildings with convective and radiant heating systems

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115355 - 115355

Published: Jan. 1, 2025

Language: Английский

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Flexibility quantification and enhancement of flexible electric energy systems in buildings

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 68, P. 106114 - 106114

Published: Feb. 22, 2023

Language: Английский

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Energy optimization algorithms for multi-residential buildings: A model predictive control application

Energy and Buildings, Journal Year: 2024, Volume and Issue: 319, P. 114562 - 114562

Published: July 17, 2024

Language: Английский

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A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 204, P. 114804 - 114804

Published: Aug. 14, 2024

Language: Английский

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Mismatch analysis of rooftop photovoltaics supply and farmhouse load: Data dimensionality reduction and explicable load pattern mining via hybrid deep learning

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124520 - 124520

Published: Sept. 21, 2024

Language: Английский

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