Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Yi Zong; Georg Martin Böning; Rui Mirra Santos; Shi You; Junjie Hu; Xue Han

doi:10.1016/j.applthermaleng.2016.11.141

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Yi Zong, Georg Martin Böning, Rui Mirra Santos, Shi You, Junjie Hu, Xue Han

Research output: Contribution to journal › Journal article › Research › peer-review

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Abstract

When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals

Original language	English
Journal	Applied Thermal Engineering
Volume	114
Pages (from-to)	1476–1486
ISSN	1359-4311
DOIs	https://doi.org/10.1016/j.applthermaleng.2016.11.141
Publication status	Published - 2016

Keywords

Active smart building
Data availability
Economic model predictive control
Modelling
Optimization
State estimation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.applthermaleng.2016.11.141

1 s2Accepted author manuscript, 2.45 MBLicence: CC BY-NC-ND

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EPIMES: Enhancing wind Power Integration through optimal use of cross-sectoral flexibility in an integrated Multi-Energy System
Bindner, H. W. (Project Manager), Zong, Y. (Project Participant), You, S. (Project Participant), Zhang, Z. (Project Participant), Chyhryn, S. (Project Participant) & Olsen, K. P. (Project Participant)
01/10/2016 → 31/03/2020
Project: Research

Cite this

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abstract = "When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system{\textquoteright}s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals",

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AU - Zong, Yi

AU - Böning, Georg Martin

AU - Santos, Rui Mirra

AU - You, Shi

AU - Hu, Junjie

AU - Han, Xue

PY - 2016

Y1 - 2016

N2 - When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals

AB - When there is a high penetration of renewables in the energy system, it requires proactive control of large numbers of distributed demand response resources to maintain the system’s reliability and improve its operational economics. This paper presents the Economic Model Predictive Control (EMPC) strategy for energy management in smart buildings, which can act as active users interacting with smart energy systems. The challenges encountered during the implementation of EMPC for active demand side management are investigated in detail in this paper. A pilot testing study shows energy savings and load shifting can be achieved by applying EMPC with weather forecast and dynamic power price signals

KW - Active smart building

KW - Data availability

KW - Economic model predictive control

KW - Modelling

KW - Optimization

KW - State estimation

U2 - 10.1016/j.applthermaleng.2016.11.141

DO - 10.1016/j.applthermaleng.2016.11.141

M3 - Journal article

SN - 1359-4311

VL - 114

SP - 1476

EP - 1486

JO - Applied Thermal Engineering

JF - Applied Thermal Engineering

ER -

Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Abstract

Keywords

UN SDGs

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EPIMES: Enhancing wind Power Integration through optimal use of cross-sectoral flexibility in an integrated Multi-Energy System

Cite this