The Impact of Temperature Change on Energy Demand: A Dynamic Panel Analysis

Cian, Enrica De; Lanzi, Elisa; Roson, Roberto

doi:10.2139/ssrn.984237

Cited by 104 publications

(35 citation statements)

References 33 publications

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“…Once a household is active (individuals present), heating/cooling might be used; it depends primarily on current conditions and individuals' preference of "comfort zones" but also socio-economic factors. De Cian et al [41] examined the interaction between income, temperature and energy demand, where an income interaction model was created, examining the income/temperature elasticity of electricity demand. Additionally, in Kane T. et al's [39] work (real world measurements), the rebound effect was greater than expected, which is attributed to the above socio-economic behaviour.…”

Section: Human Behaviour Preferences and Socio-economic Factorsmentioning

confidence: 99%

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Kleidaras

Kiprakis

Thompson

2018

Energy

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Index Terms:Demand side management Demand response Thermostatically controlled loads Markov models a b s t r a c t Demand Response (DR) is a Smart Grid technology aiming to provide demand regulation for dynamic pricing and ancillary services to the grid. Thermostatically controlled loads (TCLs) are among those with the highest potential for DR. Some of the challenges in modelling TCLs is the various factors that affect their duty cycle, mainly human behaviour and external conditions, as well as heterogeneity of TCLs (load parameters). These add an element of stochasticity, with detrimental impact on the aggregated level. Most models developed so far use Wiener processes to represent this behaviour, which in aggregated models, such as those based on Coupled Fokker-Planck Equations (CFPE), have a negligible effect as "white noise". One of the main challenges is modelling the effect of external factors on the state of TCLs' aggregated population and their impact in heterogeneity during operation. Here we show the importance of those factors as well as their detrimental effect in heterogeneity using cold loads as a case study. A bottom up detailed model has been developed starting from thermal modelling to include these factors, real world data was used as input for realistic results. Based on those we found that the duty cycle of some TCLs in the population can change significantly and thus the state of the TCLs' population as a whole. Subsequently, the accuracy of aggregation models assuming relative homogeneity and based on small stochasticity (i.e. Wiener process with typical variance 0.01) is questionable. We anticipate similar realistic models to be used for real world applications and aggregation methods based on them, especially for cold loads and similar TCLs, where external factors and heterogeneity in time are significant. DR control frameworks for TCLs should also be designed with that behaviour in mind and the developed bottom up model can be used to evaluate their accuracy.

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Section: Human Behaviour Preferences and Socio-economic Factorsmentioning

confidence: 99%

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Kleidaras

Kiprakis

Thompson

2018

Energy

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“…Changes in expenditure by tourists are modelled as changes in demand addressing the "market services sector", which includes recreational (Bigano et al, 2008); changes in health care expenditure are translated into changes in the public and private demand for the "non market services" sector, which includes health services (Bosello et al, 2006); changes in regional demand for oil, gas and electricity are modelled as changes in the demand for the output of the respective industries (De Cian et al, 2007). Changes in net forest productivity are derived from Songhen et al (2001) 1 .…”

Section: Demand-side Impactsmentioning

confidence: 99%

“…Climate-change impacts on energy demand are derived from De Cian et al (2007). This study estimates household energy demand on a macro dynamic panel dataset spanning from 1978 to 2000, for 31 countries.…”

Section: Energy Demandmentioning

confidence: 99%

La valoración económica de cambios en servicios del ecosistema: Una aplicación de la metodología CGE

Bosello

Eboli

Parrado

et al. 2011

EARN

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<span>The present study integrates Computable General Equilibrium (CGE) modelling with biodiversity services, proposing a possible methodology for assessing climate-change impacts on ecosystems. The assessment focuses on climate change impacts on carbon sequestration services provided by European forest, cropland and grassland ecosystems and on provisioning services, but provided by forest and cropland ecosystems only. To do this via a CGE model it is necessary to identify first the role that these ecosystem services play in marketable transactions; then how climate change can impact these services; and finally how the economic system reacts to those changes by adjusting demand and supply across sectors, domestically and internationally.</span>

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“…More energy will be needed in some regions for air conditioning whereas colder regions will need less energy for heating. Estimates for energy demand are based on recent literature (Fankhauser, 1995;Mendelsohn, 2000;Rosenthal et al, 1995;De Cian et al, 2007). Water use is also expected to increase, for example, due to increased irrigation needs.…”

Section: Calibration Of the Modelsmentioning

confidence: 99%

Plan or React? Analysis of Adaptation Costs and Benefits Using Integrated Assessment Models

Agrawala

Bosello

Carraro

et al. 2010

OECD Environment Working Papers

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This report examines adaptation and mitigation within an integrated framework. Global and regional costs of adaptation are assessed dynamically and the resulting benefits are quantified. This is accomplished by developing a framework to incorporate adaptation as a policy variable within three Integrated Assessment Models (IAMs); the global Dynamic Integrated model of Climate and the Economy (DICE), the Regional Integrated model of Climate and the Economy (RICE), and the World Induced Technical Change Hybrid (WITCH) model. The framework developed here takes into account investments in reactive adaptation and in adaptation "stocks", as well as investments in building adaptive capacity. This report presents the first inter-model comparison of results on adaptation costs using the emerging category of adaptation-IAMs. Results show that least-cost policy response to climate change will need to involve subsantial amounts of mitigation efforts, investments in adaptation stock, reactive adaptation measures and adaptive capacity to limit the remaining damages.

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The Impact of Temperature Change on Energy Demand: A Dynamic Panel Analysis

Cited by 104 publications

References 33 publications

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies

La valoración económica de cambios en servicios del ecosistema: Una aplicación de la metodología CGE

Plan or React? Analysis of Adaptation Costs and Benefits Using Integrated Assessment Models

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