Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

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Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. / Ruusu, Reino; Cao, Sunliang; Manrique Delgado, Benjamin; Hasan, Ala.

In: Energy Conversion and Management, Vol. 180, 15.01.2019, p. 1109-1128.

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Ruusu, Reino ; Cao, Sunliang ; Manrique Delgado, Benjamin ; Hasan, Ala. / Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. In: Energy Conversion and Management. 2019 ; Vol. 180. pp. 1109-1128.

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@article{4f4fdd8fd5724015809a786c993ca5f6,
title = "Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller",
abstract = "This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168{\%}, or by 21–75{\%} of the cost of imported electricity.",
keywords = "Energy cost minimization, Energy flexibility, Energy management, Model predictive control, Nonlinear optimization, Smart buildings",
author = "Reino Ruusu and Sunliang Cao and {Manrique Delgado}, Benjamin and Ala Hasan",
year = "2019",
month = "1",
day = "15",
doi = "10.1016/j.enconman.2018.11.026",
language = "English",
volume = "180",
pages = "1109--1128",
journal = "Energy Conversion & Management",
issn = "0196-8904",

}

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TY - JOUR

T1 - Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller

AU - Ruusu, Reino

AU - Cao, Sunliang

AU - Manrique Delgado, Benjamin

AU - Hasan, Ala

PY - 2019/1/15

Y1 - 2019/1/15

N2 - This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

AB - This article presents a new energy management system (EMS) for a variety of energy flexibility conversion, routing and storage options in buildings. The EMS uses an efficient nonlinear optimization-based model-predictive control (MPC) method, which achieves low computational complexity by utilizing successive linear programming (SLP) for continuous approximations of discrete (two-level) control problems. Whole-year simulation runs demonstrate that the method is applicable to a residential building system that has multiple energy generation, conversion and storage units with significant nonlinear interactions. Both qualitative and quantitative comparison of the simulation results with a rule-based reference control showed strong dependencies between cost and CO2 emission flexibility goals, energy selling prices and forecasting accuracy. This study shows that significant cost savings can be obtained by taking advantage of energy price fluctuations, increasing the average coefficient of performance (COP) of the heating system, and reducing passive losses in heat storage. In the simulated case study the EMS was able to improve the average COP of a heating system from 2.20 to 2.43–2.74, depending on energy cost assumptions, when compared against a rule-based control (RBC). With a performance bound of perfect forecasting the EMS was able to improve net economic outcome by 38–168%, or by 21–75% of the cost of imported electricity.

KW - Energy cost minimization

KW - Energy flexibility

KW - Energy management

KW - Model predictive control

KW - Nonlinear optimization

KW - Smart buildings

UR - http://www.scopus.com/inward/record.url?scp=85057251250&partnerID=8YFLogxK

U2 - 10.1016/j.enconman.2018.11.026

DO - 10.1016/j.enconman.2018.11.026

M3 - Article

VL - 180

SP - 1109

EP - 1128

JO - Energy Conversion & Management

JF - Energy Conversion & Management

SN - 0196-8904

ER -

ID: 30312675