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Reporting period: 2020-09-01 to 2022-02-28

In the 2030 Clean Energy for all Europeans Package from 2018, a policy frame was developed for the European Union (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 (NST) on future energy demand. We hereby understand societal developments 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 will strengthen these models while working on NSTs. Those 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 are integrated into the relevant demand-side models for quantitative analysis in the further work, 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: In the first project period the overall scenario framework for the first round of model calculations has been defined based on the needs of the existing demand-side models in the project team. A gap analysis (e.g. cross-sectoral aspects) has been carried out, which guides the further model developments and the focus analysis in step IV. Potential model linkages and parameters, to address cross-sectoral effects have been identified.
III. Policy Needs and Policy Analysis for Influencing Energy Demand Arising from NSTs: Policies which can enhance the demand decreasing trends of NSTs were analyzed to strengthen the ability to model relevant policies in demand-side models. Expert interviews with policy makers from four EC DGs and workshops were organized. 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. Three case studies corresponding to natural and artificial behavioral interventions monitored via smart meters in Italy and Poland were investigated (including COVID-19).
IV. Focus Studies on NSTs: This step deals with three Focus Studies, which dive deeper into the quantitative modeling aspects.
a. Focus Study: Prosumagers and Big Data (New Data Sources) in Energy Demand Models Related to the Built Environment.
In the first reporting period, we analyzed identified and analyzed selected household’s energy consumption patterns through machine learning methods. Moreover, we developed two models for analyzing the potential impact of prosumager behavior: (1) The model FLEX, which can be linked to the existing stock models Invert/EE-Lab and Forecast and (2) the PRIMES prosumager model, which is able to consider explicitly the link to energy markets.
b. Focus Study: Circular Economy and Digitalization in Energy Demand Models related to the Sectors Industry and Tertiary.
In the first reporting period, the scope of the focus study on a circular low-carbon industry has been defined and a model concept has been developed. The implementation of the model concept (inflow vs. stock-driven material flow analysis) has started and relevant data has been collected and prepared: A first inflow-driven modeling approach has been implemented.
For the focus study on a digitalization and market trends in the tertiary sector a model concept has been developed and finalized. Trends in the fields of remote work, e-commerce and data centers were identified due to their relevance, the impact on the energy demand and their lack in the current version of the model. The implementation of the model concept started and will be finalized in the upcoming project period.
c. Focus Study: NSTs in Transport and Tertiary Sector - The Impact of the Shared Economy. This focus study analyses the impact of the shared economy in the transport and the tertiary sector on energy demand in the EU.
In the first reporting period, a literature review on existing methods on shared mobility options has been carried out. The review helped define and refine further the modeling concept that has been developed during this reporting period.
V. 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”.
The progress on the objectives during the first reporting period contributed to the expected impacts of the newTRENDs project. 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. Novel machine learning techniques were used to leverage on large smart meter datasets for policy evaluation through uncovering patterns of household behavioral changes in power consumption associable with both artificial and natural interventions, such as COVID-19. 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. For this, we provided a comprehensive assessment of EU policy makers’ needs with regard to energy-demand modeling in view of the NSTs. Through intensive collaboration with end-users (policymakers), the completed studies identified specific policy-relevant indicators as well as policy instruments affecting NSTs that are important to be considered in the energy demand-side models.