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Abstract The aim of this review is to discuss how quantitative modelling of energy scenarios for sustainable energy transition pathways can be made more realistic by taking into account insights from the socio-technical and related literatures. The proposition is that an enriched modelling approach would focus not just on technology development and deployment, but also on feedback loops, learning processes, policy and governance, behavioural changes, the interlinkages between the energy sector and other economic sectors, and infrastructure development. The review discusses a range of socio-technical concepts with a view to how they can enrich the understanding and modelling of highly complex dynamic systems such as flexible energy systems with high shares of variable renewable energy. In this context, application of system dynamics modelling (SDM) for the analysis of energy transitions is also introduced by describing the differences between SDM and a traditional modelling approach that uses econometric and linear programming methods. A conceptual framework for this type of modelling is provided by using causal loop diagrams. The diagrams illustrate the endogenous approach of SDM – understanding and modelling the structure of a system, which is responsible for its dynamic behaviour. SDM can also capture the co-evolution of economic, policy, technology, and behavioural factors over sufficiently long time periods, which is necessary for the analysis of transition pathway dynamics. In this regard, the review summarises how socio-technical concepts can be approached in SDM and why they are relevant for the analysis of flexibility in energy systems. From a computational point of view, it could be beneficial to combine SDM with technologically detailed energy system optimization models and that could be a way forward for achieving more realistic, non-linear quantitative modelling of sustainable energy transitions.

Abstract It is of extreme importance to monitor and manage the battery health to enhance the performance and decrease the maintenance cost of operating electric vehicles. This paper concerns the machine-learning-enabled state-of-health (SoH) prognosis for Li-ion batteries in electric trucks, where they are used as energy sources. The paper proposes methods to calculate SoH and cycle life for the battery packs. We propose autoregressive integrated modeling average (ARIMA) and supervised learning (bagging with decision tree as the base estimator; BAG) for forecasting the battery SoH in order to maximize the battery availability for forklift operations. As the use of data-driven methods for battery prognostics is increasing, we demonstrate the capabilities of ARIMA and under circumstances when there is little prior information available about the batteries. For this work, we had a unique data set of 31 lithium-ion battery packs from forklifts in commercial operations. On the one hand, results indicate that the developed ARIMA model provided relevant tools to analyze the data from several batteries. On the other hand, BAG model results suggest that the developed supervised learning model using decision trees as base estimator yields better forecast accuracy in the presence of large variation in data for one battery.

The high cost of drilling deep wells is the main barrier to the widespread exploitation of deep geothermal energy. Percussive drilling is one of the significant drilling technologies used in energy exploration projects. However, there is no good quantitative understanding of how much energy in percussive drilling is consumed in pulverization, heating, the kinetic energy of particles, acoustic emission, etc. In this study, energy efficiency is quantitatively investigated to understand the percussive drilling process better. The dynamic percussive drilling was evaluated using a modified split Hopkinson pressure bar (SHPB) system and non-contact measurements. The amount of energy dissipated in different processes and the overall energy efficiency was estimated for Kuru granite, Balmoral granite, and Kivijärvi gabbro. The energy spent on the kinetic energy Ek of fragments was evaluated using a high-speed camera, whereas the energy consumed on heat or the thermal energy Et was obtained by high-speed infrared imaging. The cracking energy Ec was measured by using the surface energy of rock and the total newly created surface areas. The results indicate that the fragment size distribution of these three rocks generally varies with the penetration speed, and the fragmentation level of these rocks increases with the penetration speed. The input energy and the energy consumption grow with the increase of the penetration speed. The proportions of Et, Ek, and Ec in the total energy consumption for these three rocks increase with the penetration speed. The energy efficiency obtained from the dynamic indentation experiments for the three rocks generally increases with the penetration speed and almost approaches a limit value when the penetration speed is high. A model is improved to describe the relationship between energy efficiency and penetration speed quantitatively. Therefore, the penetration process should be optimized to balance the high drilling efficiency and the low energy consumption.

Abstract Strategic niches are protected spaces for emerging technologies, where expectations are articulated, social networks built, and learning occurs. Although Strategic Niche Management (SNM) can be done in a directed, strategic manner, more diffuse, loosely connected and self-organizing niches also exist. We explore such niches in a particular setting: a social media discussion forum − a Facebook group − set up for an open discussion on the reform of national-level energy policy in Finland. We focus on discussions related to two renewable energy technologies: biogas and ground-source heat pumps. We conduct Social Network Analysis and quantitative as well as qualitative content analysis of the social media material to ask what kind of SNM happens in these discussions. Our results indicate that the discussion networks may be conducive for wide engagement and incorporation of new ideas, while also containing sub-groups that may foster learning. However, the discussions are highly centralized around a few active discussants and focused on the present-day situation, drawing from specific local and national experiences and technical details, despite the original aim of the group to induce forward-looking debates on energy policy. The articulation of future expectations is not a predominant feature of the discussions related to these two technologies. Still, the quantitative content analysis reveals extensive agreement in their framing as sustainable future energy solutions, while the qualitative analysis also points to critical debates that may support learning and further development of shared expectations.

Abstract The objective of this article is to design and plan sustainable bio-ethanol supply chain. Modeling supply chains that achieve economic, social and environmental feasibility through production, process and energy efficiency is a challenge. Life cycle assessment that is coupled with techno-economic optimization of bio-ethanol supply chain is an alternative solution to achieve sustainability. A simulation of the biomass conversion is used to find process parameters of the conversion technology. The results show that the unified model is capable of minimizing both CO 2 emissions and energy and utility consumptions. In addition, the supply chain is capable of contributing to local economy through jobs creation. While the model is quite comprehensive, the future research recommendation on energy integration and global sustainability is proposed.

AbstractThe fight against global warming requires novel approaches for the defossilisation of industrial processes, and the limitation of global warming requires options for negative carbon dioxide (CO2) emissions. The production of carbon fibre (CF) is an energy-intensive chain of processes which cause CO2 emissions. Having in mind the high market growth for CF composite materials, CF production might stand against the fight against global warming. CF also offers a huge mitigation opportunity, as CF contain up to 95–98wt% of pure carbon. This study investigates possible ways to link CF production to atmospheric CO2, enabling negative CO2 emissions through CF manufacturing. Production value chains for CF based on poly(acrylonitrile) (PAN) and pitch, the two most important CF precursor materials, are developed and analysed regarding their energy and mass balances. The PAN value chain is further assessed regarding a first economic estimation of CF production cost with atmospheric CO2 as carbon source. The results show that production costs per ton CO2 removed might be unattractive at 2949 €/tCO2 in 2050. However, from a CF perspective, production cost of 10.3 €/kgCF in 2050 might enable a business case for electricity-based CF production from atmospheric CO2 in the future. Each ton of CF produced can store about 3.5 tCO2 due to a very high carbon share in the final product. With an increasing market for CF, a total negative emission potential of at least 0.7 GtCO2 per year can be enabled by 2050. Further research opportunities are discussed.

Abstract To enter a market and scale up, entrant firms often need to cooperate with their incumbent competitors, so they are in coopetition with them. Our goal is to increase the understanding of the antecedents of coopetition and the ways in which new entrant firms navigate coopetitive tensions with incumbents. Moreover, we are interested in the impacts that coopetition has on the value creation and value appropriation of new entrant firms. So far, most literature on cooperation and coopetition in energy markets has provided the perspective of the incumbents. To study the issues empirically, we interviewed 15 demand response (DR) entrants. These firms operate in Finnish energy markets, providing automated DR services, in which Finland is a forerunner country. According to our results, collaboration between new entrant DR firms and energy incumbents was needed in order to establish the new markets. In addition, cooperation with incumbents was beneficial to DR entrants since they were able to gain new customers and increase the efficiency of their resource use due to, for example, common technological development activities. We found that the structure of energy markets was an important factor in shaping the market entry of DR entrants. According to our results, new entrants can enter electricity markets without much cooperation with the incumbents, but cooperation is necessary in natural monopoly district heating markets. As new EU regulations will enhance automated DR services, the results of this study have relevance in other EU Member States where automated DR markets have not yet been established.

Buildings are a major contributor to climate change. Use phase has traditionally been the focus area, but the importance of construction-phase has increased with the emergence of energy-efficient buildings. Life-cycle assessment (LCA) is arguably the best method to assess and analyze the emissions caused by buildings. However, within LCA there are two very different approaches – process LCA and input–output (IO) LCA – which lead to different results. When looking at the scale of published LCA results, it is evident that IO LCAs are placed at the top end, and process LCAs at the bottom end. It is thus questionable whether LCA can provide data that can be used for decision-making and policy formation. This study takes a step toward filling this gap by presenting a comparison of process and IO LCA results of the pre-use phase of a residential concrete element building in Finland. Exactly the same scope is utilized in order to maximize comparability. The results depict how the two main LCA methods produce significantly different results. The implications of acknowledging this are discussed. The results fall in midway between the extremes published using the two methods but still deviate from each other by a multiplier of almost 2.