Periodic Reporting for period 2 - NEWTRENDS (NEW TRENDS IN ENERGY DEMAND MODELING)
Periodo di rendicontazione: 2022-03-01 al 2023-10-31
In the 2030 Clean Energy for all Europeans Package from 2018, a policy frame was developed for the EU to guide the achievement of the Paris Agreement and other objectives related to supply security and a competitive energy system. The 2050 Long-term Strategy of the EU develops possible scenarios to a climate neutral EU in 2050 with scenarios aiming at the full deployment of all technology options, while other scenarios assume an increase in climate awareness of EU citizens translating into lifestyle changes and consumer choices, as well as a more circular economy.
In this project we assess the impact of New Societal Trends on future energy demand. We hereby understand societal trends as arising from general Megatrends, which can have potentially large (increasing or decreasing) impacts on energy consumption as well as cross-sectoral demand shifts because they are not simply the extrapolation of already presently observed trends ("continuous or linear trends") but may take up speed when they are embraced by larger parts of the society ("disruptive or non-linear trends"). Such trends include in particular:
• Transition of Consumers to Prosumagers
• Move towards a Circular Economy and a Low-carbon industry
• Digitalization of the Economy and of private lives
• Trends towards a Shared Economy
Our approach relies on several well-established models (bottom-up energy demand and macro-models), which have all been used extensively in the European context for projections up to 2050 and beyond (EU28 and individual Member States). We strengthened these models by enhancing them to model new societal trends. Those models include INVERT/EE-Lab, run by TUW and e-think, the FORECAST bottom-up model family, run by Fraunhofer in cooperation with TEP, the PRIMES energy system model, run by E3M, with focus on PRIMES-BuiMo, as well as the PRIMES-TREMOVE transport model and GEM-E3 run by E3M.
In this project we assess the impact of New Societal Trends on future energy demand. We hereby understand societal trends as arising from general Megatrends, which can have potentially large (increasing or decreasing) impacts on energy consumption as well as cross-sectoral demand shifts because they are not simply the extrapolation of already presently observed trends ("continuous or linear trends") but may take up speed when they are embraced by larger parts of the society ("disruptive or non-linear trends"). Such trends include in particular:
• Transition of Consumers to Prosumagers
• Move towards a Circular Economy and a Low-carbon industry
• Digitalization of the Economy and of private lives
• Trends towards a Shared Economy
Our approach relies on several well-established models (bottom-up energy demand and macro-models), which have all been used extensively in the European context for projections up to 2050 and beyond (EU28 and individual Member States). We strengthened these models by enhancing them to model new societal trends. Those models include INVERT/EE-Lab, run by TUW and e-think, the FORECAST bottom-up model family, run by Fraunhofer in cooperation with TEP, the PRIMES energy system model, run by E3M, with focus on PRIMES-BuiMo, as well as the PRIMES-TREMOVE transport model and GEM-E3 run by E3M.
I. Selection of NSTs and Quantification of Impacts on Energy Demand: This step encompassed a process of selecting clusters of NSTs, which were integrated into the relevant demand-side models for quantitative analysis in the further WPs, based on literature review and stakeholder workshops. These trends and their potential importance and disruptiveness were discussed and narratives describing their mechanisms were developed.
II. Development of Transition Pathways for NSTs and Methodological Improvement in Modeling such Trends: A gap analysis (e.g. cross-sectoral aspects) has been carried out to identify to which extend the models were able to capture NSTs before their enhancement. This guided the further model developments and the focus analysis in step IV.
III. Policy Needs and Policy Analysis for Influencing Energy Demand Arising from NSTs: Policies, which can enhance the demand decreasing trends of NSTs, were analysed through literature research, expert interviews with policy makers from four EC DGs and workshops. As a result, policy challenges that need to be addressed by the energy demand models were identified and their representation in the energy demand models was assessed. Furthermore, a novel machine learning techniques that can be used to leverage on large smart meter data for policy evaluation was developed.
IV. Within the focus studies we investigated how energy demand models are to be improved to represent such NSTs. We further aimed at representing in energy demand models policies that can influence such trends in the light of the Energy Efficiency First (EE1) Principle brought forward in the EU policy framework.
• Prosumaging in residential buildings (WP 5): We investigated how different prices impact decision making of prosumagers specifically examining what load shifting potential prosumagers have, given different fiscal incentives and consequently how this affects energy consumption.
• Circular economy in the industry sector (WP 6): we improved the modelling of the impact of a circular economy on the industry sector via endogenous consideration of material stocks and flows related to the emission intensive materials steel and cement for buildings. In addition, the developed model considers cross-sectoral impacts by soft linking the building stock model, Invert/EE-Lab, and the industry model, FORECAST.
• Digitalization of the tertiary sector (WP 6): we implemented model enhancements to reflect the new trends of teleworking, e-commerce, building automation and data centers. We established cross-sectoral linkages to the residential and the transport sector through indicators in the fields of e-commerce and teleworking.
• Sharing economy in the transport sector (WP 7): we operationalized a satellite shared mobility module with PRIMES-TREMOVE. We quantified the energy requirements per mobility service and explicitly represented the choice of drivers for alternative options.
V. Overall scenarios and macro-economic modeling (WP 3): Based on the sectoral results we provide four scenarios that show how the NSTs might impact future energy demand. Furthermore, we modelled the macro-economic effects in these scenarios. Based on the sectoral results a bottom-up representation of key elements of energy system including transportation / mobility options, power generation energy efficiency programs was modelled
VI. Communication and dissemination: In the first reporting period, the newTRENDs project organized its first stakeholder workshop on ‘Policies for new trends in energy demand modeling”.
II. Development of Transition Pathways for NSTs and Methodological Improvement in Modeling such Trends: A gap analysis (e.g. cross-sectoral aspects) has been carried out to identify to which extend the models were able to capture NSTs before their enhancement. This guided the further model developments and the focus analysis in step IV.
III. Policy Needs and Policy Analysis for Influencing Energy Demand Arising from NSTs: Policies, which can enhance the demand decreasing trends of NSTs, were analysed through literature research, expert interviews with policy makers from four EC DGs and workshops. As a result, policy challenges that need to be addressed by the energy demand models were identified and their representation in the energy demand models was assessed. Furthermore, a novel machine learning techniques that can be used to leverage on large smart meter data for policy evaluation was developed.
IV. Within the focus studies we investigated how energy demand models are to be improved to represent such NSTs. We further aimed at representing in energy demand models policies that can influence such trends in the light of the Energy Efficiency First (EE1) Principle brought forward in the EU policy framework.
• Prosumaging in residential buildings (WP 5): We investigated how different prices impact decision making of prosumagers specifically examining what load shifting potential prosumagers have, given different fiscal incentives and consequently how this affects energy consumption.
• Circular economy in the industry sector (WP 6): we improved the modelling of the impact of a circular economy on the industry sector via endogenous consideration of material stocks and flows related to the emission intensive materials steel and cement for buildings. In addition, the developed model considers cross-sectoral impacts by soft linking the building stock model, Invert/EE-Lab, and the industry model, FORECAST.
• Digitalization of the tertiary sector (WP 6): we implemented model enhancements to reflect the new trends of teleworking, e-commerce, building automation and data centers. We established cross-sectoral linkages to the residential and the transport sector through indicators in the fields of e-commerce and teleworking.
• Sharing economy in the transport sector (WP 7): we operationalized a satellite shared mobility module with PRIMES-TREMOVE. We quantified the energy requirements per mobility service and explicitly represented the choice of drivers for alternative options.
V. Overall scenarios and macro-economic modeling (WP 3): Based on the sectoral results we provide four scenarios that show how the NSTs might impact future energy demand. Furthermore, we modelled the macro-economic effects in these scenarios. Based on the sectoral results a bottom-up representation of key elements of energy system including transportation / mobility options, power generation energy efficiency programs was modelled
VI. Communication and dissemination: In the first reporting period, the newTRENDs project organized its first stakeholder workshop on ‘Policies for new trends in energy demand modeling”.
NSTs were assessed from the sectoral perspectives and a corresponding gap analysis of energy demand models was carried out. Furthermore, we identified and conceptualized methodological approaches to implement these NSTs in the existing demand models. Overall, this is expected to contribute to the accurate and holistic mapping of the demand side. Moreover, we assessed to what extent the energy demand-side models are following the changes in the evolving EU policy framework due to NSTs. Thanks to a better understanding of models’ alignment with the policy needs and expectations, the future developments of energy-demand side models are now more likely to progress in line with the information needs of policy makers. The model enhancements, particularly performed in the focus studies, now allow for a more accurate and holistic mapping and modeling of the demand side and a better assessment of the impact of NSTs in the different sectors. The four modelled scenarios showcase how NSTs might impact future energy demand and particularly the role that meaningful policies play in shaping the future energy demand.