Optimizing electricity consumption of buildings in a microgrid through demand response

R. Morales González; S. Shariat Torbaghan; M. Gibescu; J.F.G. Cobben; Martijn A. Bongaerts; M. de Nes-Koedam; W. Vermeiden

doi:10.1109/PTC.2017.7980983

Optimizing electricity consumption of buildings in a microgrid through demand response

R. Morales González, S. Shariat Torbaghan, M. Gibescu, J.F.G. Cobben, Martijn A. Bongaerts, M. de Nes-Koedam, W. Vermeiden

Electrical Energy Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

210 Downloads (Pure)

Abstract

This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost- and energy performance of the system, but also the utilization of renewable energy sources in the microgrid.

Original language	English
Title of host publication	2017 IEEE Manchester PowerTech, Powertech 2017
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	1-6
Number of pages	6
ISBN (Electronic)	978-1-5090-4237-1
ISBN (Print)	978-1-5090-4238-8
DOIs	https://doi.org/10.1109/PTC.2017.7980983
Publication status	Published - 13 Jul 2017
Event	12th IEEE PES PowerTech Conference - University of Manchester, Manchester, United Kingdom Duration: 18 Jun 2017 → 22 Jun 2017 Conference number: 12 http://ieee-powertech.org/

Conference

Conference	12th IEEE PES PowerTech Conference
Abbreviated title	PowerTech 2017
Country/Territory	United Kingdom
City	Manchester
Period	18/06/17 → 22/06/17
Other	Towards and Beyond Sustainable Energy Systems
Internet address	http://ieee-powertech.org/

Keywords

Demand response
genetic algorithm
local RES integration
physical system modeling
smart microgrids

UN SDGs

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

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10.1109/PTC.2017.7980983

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Morales González, R., Shariat Torbaghan, S., Gibescu, M., Cobben, J. F. G., Bongaerts, M. A., de Nes-Koedam, M., & Vermeiden, W. (2017). Optimizing electricity consumption of buildings in a microgrid through demand response. In 2017 IEEE Manchester PowerTech, Powertech 2017 (pp. 1-6). [7980983] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PTC.2017.7980983

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abstract = "This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost- and energy performance of the system, but also the utilization of renewable energy sources in the microgrid. ",

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Morales González, R, Shariat Torbaghan, S, Gibescu, M, Cobben, JFG, Bongaerts, MA, de Nes-Koedam, M & Vermeiden, W 2017, Optimizing electricity consumption of buildings in a microgrid through demand response. in 2017 IEEE Manchester PowerTech, Powertech 2017., 7980983, Institute of Electrical and Electronics Engineers, Piscataway, pp. 1-6, 12th IEEE PES PowerTech Conference, Manchester, United Kingdom, 18/06/17. https://doi.org/10.1109/PTC.2017.7980983

Optimizing electricity consumption of buildings in a microgrid through demand response. / Morales González, R.; Shariat Torbaghan, S.; Gibescu, M. et al.

2017 IEEE Manchester PowerTech, Powertech 2017. Piscataway : Institute of Electrical and Electronics Engineers, 2017. p. 1-6 7980983.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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AU - Shariat Torbaghan, S.

AU - Gibescu, M.

AU - Cobben, J.F.G.

AU - Bongaerts, Martijn A.

AU - de Nes-Koedam, M.

AU - Vermeiden, W.

N1 - Conference code: 12

PY - 2017/7/13

Y1 - 2017/7/13

N2 - This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost- and energy performance of the system, but also the utilization of renewable energy sources in the microgrid.

AB - This paper optimizes the thermodynamic behavior of buildings through demand response (DR) by operating their mechanical heating/cooling systems at 50% or 100% output capacity on a 15-minute basis. The optimization's objective is either minimizing cost or net electricity consumption, considering hourly prices and renewable energy resource availability in the local microgrid. The proposed DR framework combines thermodynamic models with an automated, genetic-algorithm based optimization, resulting in demonstrable benefits in terms of cost and energy efficiency for the end-users. The optimal DR schedule with multiple heating/cooling output capacity is compared against an unoptimized, business-as-usual scenario and against a DR schedule which allows only a binary operation. Results show that flexibility can be harnessed from the buildings' thermal mass, and that a finer temporal granularity not only improves the cost- and energy performance of the system, but also the utilization of renewable energy sources in the microgrid.

KW - Demand response

KW - genetic algorithm

KW - local RES integration

KW - physical system modeling

KW - smart microgrids

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M3 - Conference contribution

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SP - 1

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BT - 2017 IEEE Manchester PowerTech, Powertech 2017

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - 12th IEEE PES PowerTech Conference

Y2 - 18 June 2017 through 22 June 2017

ER -

Optimizing electricity consumption of buildings in a microgrid through demand response

Abstract

Conference

Keywords

UN SDGs

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