• Energy Research

Abstract As energy models become more and more powerful, they are increasingly used to support energy policymaking. Although modelling has been used for policy advice for many years, there is little knowledge about how computer-based models actually influence policymaking, and to what extent policymakers influence the modelling process. Here, we empirically investigate (i) whether, how and when models influence the policymaking process, and (ii) whether, how and when policymakers influence the design, use and results of energy modelling. We analysed modelling and policy documents and conducted thirty-two interviews with different stakeholder groups in five different European jurisdictions. We show that models are used and have an impact on policymaking, especially by assessing impacts and supporting target setting, and sometimes by exploring policy options to reach these targets. We also show that policymakers influence models and modellers, especially by affecting data and assumptions, as well as the study scope, and by deciding how the modelling results are used. Hence, energy modelling and policymaking influence each other. In their exploratory mode, models can help investigate policy options and ambitious target setting. However, models can also be instrumentalised to justify already decided policies and targets. Our study implies that greater transparency, including open-source code and open data, and transdisciplinary elements in modelling could increase model legitimacy and impact in policymaking.

The process of decision-making on climate and energy policy is a challenging task, which is affected by an important number of internal and external factors that influence the dynamics of the energy system. It is critical to investigate and understand how a specific policy instrument affects various sectors and to employ model-based scenarios to examine potential environmental and energy-related trends influenced by uncertain dynamics. In this report, we have strived for the development of a comprehensive set of narratives and scenarios that will be used in the upcoming modeling exercises to produce outcomes related to the assessment of the decarbonization potential of the energy citizenship concept. In order to reach our goal, we explored the literature around the development of decarbonization narratives and scenarios, using as a starting point insights from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Global Warming of 1.5°C (SR1.5) and the concept of Shared Socioeconomic Pathways (SSPs) to produce the most up-to-date and policy-relevant evidence on the contribution of energy citizenship in reaching climate neutrality. In particular, the five SSPs present a set of five qualitative descriptions of future changes in demographics, human development, economy and lifestyle, policies and institutions, technology, and environment and natural resources: SSP1: “Sustainability-Taking the Green Road” SSP2: “Middle of the Road” SSP3: “Regional Rivalry-A Rocky Road” SSP4: “Inequality-A Road Divided” SSP5: “Fossil-fueled Development-Taking the Highway” Based on the SSPs, we formulated three narratives, which describe future systemic changes of the society and economy in general, providing with “future worlds” that will be inhabited by citizens: “A unified world” (Citizens at the core of the energy transition, inclusive development). “A fragmented world” (Regional conflicts, countries prioritize domestic issues). “A familiar world” (Reference narrative). In parallel, we brought the citizens to the forefront with the aim of also building “people-centric” narratives, based on energy citizenship trends & patterns previously identified: “Power to the People” (Active participation in the energy market). “Band Together” (Collective expressions of energy citizenship). “Habitual Creatures” (Actions towards energy efficiency). “People to the Streets” (Political activities). “Business as usual” (Reference narrative). Finally, as a next step, and through the combination of “future worlds” and “people-centric” narratives, we will formulate specific quantitative scenarios, which will be modeled with the use of the ENCLUDE modeling ensemble, i.e., the Agent-based Technology adOption Model (ATOM), the Dynamic high-Resolution dEmand-sidE Management (DREEM) model, and the Integrated Model to Assess the Global Environment (IMAGE). The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither CINEA nor the European Commission is responsible for any use that may be made of the information contained therein.

This report examines the structural barriers preventing investment in energy efficiency measures in Europe’s Private Rented Sector (PRS) housing stock. The analysis is undertaken with reference to the broader trends in private renting, the regulatory landscape that currently exists, and the need to reduce social vulnerability and income poverty more generally. An introduction detailing historical and geographical trends in Europe, using case studies, and elaborating upon research and policy analysis from previous ENPOR project reports, serves to provide the starting point of the review. Following desk-based reviews of academic and grey literature, we identified Financial, Political/Regulatory, Social, and Geographical factors as key barriers to the implementation of energy efficiency policies in the PRS, which provide the backbone structure of this review. Several strands of analysis are drawn upon, including expert viewpoints and a stakeholder survey. The primary survey was conducted with a range of stakeholders working in the field of energy poverty, energy efficiency, housing, and decarbonisation, and served to generate primary data on knowledge of policies, ratings of importance on the identified barriers and governance scales, and understandings of the effects of policy on vulnerable groups. Partners from the ENPOR Consortium also provided expert situated viewpoints, which were drawn together to provide a holistic overview of factors contributing to the key barriers, as well as suggesting potential solutions from a multi-stakeholder perspective, supplemented by the survey’s findings. A common theme running throughout our analyses and recommendations is that solutions to energy poverty in the private rented sector are situated across the barriers, and are ultimately financial, social, political/regulatory and technical. Although a practical way of identifying structural factors that can prevent investment in energy efficiency, this is where we reach the limits of the conceptual notion of ‘barriers’ as an explanatory tool for understanding the persistent energy poverty, housing quality and energy efficiency related challenges. The authors would like to acknowledge the support from the EC. The authors would like to thank the stakeholders that participated in the online survey. The content of this report is the sole responsibility of its authors and does not necessary reflect the views of the EC.

Abstract Increasing shares of renewable energy sources and managing total demand are considered pivotal for energy transitions that fundamentally re-envisage the electricity system. A key challenge of such transitions is integrating and absorbing increased shares of non-dispatchable renewable energy sources, without jeopardizing the security and the reliability of the electricity system. To this end, key solutions include the introduction of demand-side management. However, so far, demand-side management modeling at the building sector has been proven challenging, as existing models are not flexible enough to incorporate a wide set of modeling features and guiding principles, while including all important aspects of end-use. This paper presents a new dynamic high-resolution demand-side management model which brings together all the key features and guiding principles of demand-side management modeling. The novelty of the model lies mainly in its modularity, as the main modeling framework is decomposed into individual modules, hierarchically dependent on components embodying standards and design rules, allowing for multiple configurations and computational efficiency. To demonstrate its applicability the model was used to explore benefits of demand-flexibility for consumers in the residential sector in Greece. Simulation results showed that the flexibility to increase self-consumption can be brought to the Greek electricity sector without a need for significant changes in the current market design, and for consumers to sacrifice thermal comfort and energy services.

Abstract In Greece, the renewable energy potential and a low-quality building stock constitute the background of a possible low-carbon energy transition. This transition, however, faces significant uncertainties, ranging from long-term effects of the ongoing economic recession and technological lock-ins, to the stability of the regulatory framework and issues of public acceptance. Such uncertainties may eventually give rise to significant barriers to, as well as severe economic and social consequences of, the envisaged transition. Here, in a structured approach to eliciting the knowledge embedded in stakeholders, we identify such risks and explore their dynamics and role in a sustainable transition to a power system that is based on large-scale solar projects and prosuming in the residential sector. We then employ a modelling ensemble, consisting of a macroeconomic dynamic stochastic general equilibrium model and a business strategy assessment model, in order to quantify and evaluate the extent of the identified risks’ impact.

Abstract Energy system models are advancing rapidly. However, it is not clear whether models are becoming better, in the sense that they address the questions that decision-makers need to be answered to make well-informed decisions. Therefore, we investigate the gap between model improvements relevant from the perspective of modellers compared to what users of model results think models should address. Thus, we ask: What are the differences between energy model improvements as perceived by modellers, and the actual needs of users of model results? To answer this question, we conducted a literature review, 32 interviews, and an online survey. Our results show that user needs and ongoing improvements of energy system models align to a large degree so that future models are indeed likely to be better than current models. We also find mismatches between the needs of modellers and users, especially in the modelling of social, behavioural and political aspects, the trade-off between model complexity and understandability, and the ways that model results should be communicated. Our findings suggest that a better understanding of user needs and closer cooperation between modellers and users is imperative to truly improve models and unlock their full potential to support the transition towards climate neutrality in Europe.

Although energy system models have become more complex, it does not necessarily mean that they are better suited to answer the questions, or address the challenges, faced by decision- and policymakers. In this report, we aim to tackle such critical issues and challenges of the European energy transition towards climate neutrality by 2050, with the user-driven updated SENTINEL modelling ensemble. Specifically, we showcase the applicability and usefulness of the SENTINEL modelling suite in the context of three case studies, a. a Continental level case study (European Union, Iceland, Norway, Switzerland, the United Kingdom, and some Balkan countries), b. a Regional level case study (Nordic countries), and c. a National level case study (Greece). Specifically, this report provides details on input data, as well as model linkages and results, and serves two purposes. It provides (i). detailed specifications for the application of the SENTINEL models in the context of policy-relevant scenarios and energy and climate targets, and (ii). answers to stakeholders’ critical research questions through scientific evidence from the SENTINEL models. Modelling results relevant to the power sector’s transformation showcase that the transition to a low-carbon power sector would need to consider potential lock-ins to intermediate technologies, such as natural gas, which could decrease European energy security, and increase import dependency. On the demand side, the potential for energy demand reduction in the European transport sector is large, while the industry sector presents inertia. However, electrification in both sectors is expected to become significant, which would decrease fossil-fuel extraction and use, and consequently direct fossil carbon dioxide emissions. Furthermore, achieving decarbonisation in the building sector by 2050 is possible but would require a higher annual rate of high-efficiency renovations and new buildings than currently prescribed, which would also require strong political support to accelerate the implementation of measures. Overall, increasing electrification across all demand sectors is expected to cause changes in total and hourly power demand, which could potentially increase peak demand. In this context, sector coupling can provide the necessary flexibility to the power system and ensure an adequate balance between energy supply and demand. Regarding the environmental impacts of the energy transition, we highlight that greenhouse-gas emission reductions should not be looked at solely, as the effect of the energy transition on other aspects (such as for example, human toxicity, human health, water depletion, particulate matter formation, terrestrial acidification, etc.) may be negative. On top of that, risks regarding the availability of critical raw materials should be taken into account to avoid scarcity of raw materials required for key new renewable technologies. Finally, on the socio-economic aspect, we show that although a people-powered, decentralised energy system has the highest system cost, it has the largest economy-wide welfare benefits, including positive aggregate EU27+ employment effects by 2030 and by 2050. The authors would like to acknowledge the support from the EC. The authors would like to thank the SENTINEL colleagues that contributed to specific sections relevant to their models' application to the SENTINEL case studies. The content of this report is the sole responsibility of its authors and does not necessary reflect the views of the EC.