Thermal request optimization of a smart district heating system

Name: Thermal request optimization of a smart district heating system
Author: Ozpoyraz, I., Ay, D., Aykut, A., Yanikoglu, Ihsan, Karasu, Mehmet Berk

İsim	Thermal request optimization of a smart district heating system
Yazar	Ozpoyraz, I., Ay, D., Aykut, A., Yanikoglu, Ihsan, Karasu, Mehmet Berk
Basım Tarihi:	2024-12-01
Basım Yeri	- Elsevier
Konu	Genetic algorithm, Optimization, Peak shaving, Smart valve, District heating
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0378-7788
Kayıt Numarası	59ac15b3-0994-49db-b92b-14c3635fbfc2
Lokasyon	Industrial Engineering
Tarih	2024-12-01
Notlar	Turkish Science Academy ; TÜBİTAK
Örnek Metin	This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.
DOI	10.1016/j.enbuild.2024.114841
Cilt	324

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Thermal request optimization of a smart district heating system

Yazar Ozpoyraz, I., Ay, D., Aykut, A., Yanikoglu, Ihsan, Karasu, Mehmet Berk

Basım Tarihi 2024-12-01

Basım Yeri - Elsevier

Konu Genetic algorithm, Optimization, Peak shaving, Smart valve, District heating

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0378-7788

Kayıt Numarası 59ac15b3-0994-49db-b92b-14c3635fbfc2

Lokasyon Industrial Engineering

Tarih 2024-12-01

Notlar Turkish Science Academy ; TÜBİTAK

Örnek Metin This paper proposes a solution approach to manage the heating plans of tenants served by a district heating plant located in Sweden. To do that, the daily temperature request of each household in the associated pilot region is obtained, and the daily temperature profile of each household is optimized with the help of the proposed decision support system and smart valves. The hot water inflow rates of radiators are remotely controlled via smart valves at each flat to minimize the total energy consumption, carbon emission and cost associated with the energy consumption of the district heating plant. We aim to shave the peak demands while fully satisfying the temperature requests of households without violating their thermal comfort. Peak demand shaving is achieved by generating preheating schedules via mathematical optimization and using the thermal storage potential of the insulated flats. The resulting mathematical optimization model presents significant computational challenges that cannot be efficiently solved using optimization solvers within a reasonable time limit. To this end, we develop three genetic algorithm approaches that are computationally scalable for realistically-sized instances and verified to yield near-optimal solutions for the test instances. Extensive numerical analyses show the effectiveness of the proposed approach and the genetic algorithm since we yield significant carbon emission reduction and cost savings compared with the method that the experts of the utility company propose.

DOI 10.1016/j.enbuild.2024.114841

Cilt 324