Reducing the carbon footprint of house heating through model predictive control - A simulation study in Danish conditions

Pierre Jacques Camille Vogler-Finck, Rafal Wisniewski, Petar Popovski

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

5 Citationer (Scopus)

Resumé

Around the world, electricity systems are transitioning towards renewable energy to meet humanity's climate change mitigation targets. However, in a pre-transition system, the carbon intensity of power exhibits strong variations over time, which calls for load shifting to times when its impact is lower. In this work, the case of heating in single-family houses is studied, using Model Predictive Control (MPC) to optimise multi-zone operation. Low inertia heating is used, and simulations are made upon three different insulation level using historical grid and climate data from Denmark. The results show that energy and CO2 optimisation are relevant objectives for predictive control for lowering the carbon footprint of heating, while SPOT price optimisation is comparatively undesirable. However, benefits of energy optimisation were questioned, as a well-tuned PID control might have had similar performance. Nevertheless, gains from CO2 optimisation in recent houses highlight the importance of considering the average carbon intensity of energy used, in addition to the amount of energy itself, when aiming to reduce the carbon footprint.
OriginalsprogEngelsk
TidsskriftSustainable Cities and Society
Vol/bind42
Sider (fra-til)558-573
Antal sider16
ISSN2210-6707
DOI
StatusUdgivet - 1 okt. 2018

Citer dette

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abstract = "Around the world, electricity systems are transitioning towards renewable energy to meet humanity's climate change mitigation targets. However, in a pre-transition system, the carbon intensity of power exhibits strong variations over time, which calls for load shifting to times when its impact is lower. In this work, the case of heating in single-family houses is studied, using Model Predictive Control (MPC) to optimise multi-zone operation. Low inertia heating is used, and simulations are made upon three different insulation level using historical grid and climate data from Denmark. The results show that energy and CO2 optimisation are relevant objectives for predictive control for lowering the carbon footprint of heating, while SPOT price optimisation is comparatively undesirable. However, benefits of energy optimisation were questioned, as a well-tuned PID control might have had similar performance. Nevertheless, gains from CO2 optimisation in recent houses highlight the importance of considering the average carbon intensity of energy used, in addition to the amount of energy itself, when aiming to reduce the carbon footprint.",
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Reducing the carbon footprint of house heating through model predictive control - A simulation study in Danish conditions. / Vogler-Finck, Pierre Jacques Camille; Wisniewski, Rafal; Popovski, Petar.

I: Sustainable Cities and Society, Bind 42, 01.10.2018, s. 558-573.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Reducing the carbon footprint of house heating through model predictive control - A simulation study in Danish conditions

AU - Vogler-Finck, Pierre Jacques Camille

AU - Wisniewski, Rafal

AU - Popovski, Petar

PY - 2018/10/1

Y1 - 2018/10/1

N2 - Around the world, electricity systems are transitioning towards renewable energy to meet humanity's climate change mitigation targets. However, in a pre-transition system, the carbon intensity of power exhibits strong variations over time, which calls for load shifting to times when its impact is lower. In this work, the case of heating in single-family houses is studied, using Model Predictive Control (MPC) to optimise multi-zone operation. Low inertia heating is used, and simulations are made upon three different insulation level using historical grid and climate data from Denmark. The results show that energy and CO2 optimisation are relevant objectives for predictive control for lowering the carbon footprint of heating, while SPOT price optimisation is comparatively undesirable. However, benefits of energy optimisation were questioned, as a well-tuned PID control might have had similar performance. Nevertheless, gains from CO2 optimisation in recent houses highlight the importance of considering the average carbon intensity of energy used, in addition to the amount of energy itself, when aiming to reduce the carbon footprint.

AB - Around the world, electricity systems are transitioning towards renewable energy to meet humanity's climate change mitigation targets. However, in a pre-transition system, the carbon intensity of power exhibits strong variations over time, which calls for load shifting to times when its impact is lower. In this work, the case of heating in single-family houses is studied, using Model Predictive Control (MPC) to optimise multi-zone operation. Low inertia heating is used, and simulations are made upon three different insulation level using historical grid and climate data from Denmark. The results show that energy and CO2 optimisation are relevant objectives for predictive control for lowering the carbon footprint of heating, while SPOT price optimisation is comparatively undesirable. However, benefits of energy optimisation were questioned, as a well-tuned PID control might have had similar performance. Nevertheless, gains from CO2 optimisation in recent houses highlight the importance of considering the average carbon intensity of energy used, in addition to the amount of energy itself, when aiming to reduce the carbon footprint.

KW - Carbon footprint

KW - Heating

KW - Model predictive control

KW - Single-family house

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