The dynamics of copper intercalated molybdenum ditelluride

Layered transition metal dichalcogenides are emerging as key materials in nanoelectronics and energy applications. Predictive models to understand their growth, thermomechanical properties and interactions with metals are needed in order to accelerate their incorporation into commercial products. Interatomic potentials enable large-scale atomistic simulations at the device level, beyond the range of applications of first principle methods. We present a ReaxFF reactive force field to describe molybdenum ditelluride and its interactions with copper. We optimized the force field parameters to describe the properties of layered MoTe2 in various phases, the intercalation of Cu atoms and clusters within its van der Waals gap, including a proper description of energetics, charges and mechanical properties. The training set consists of an extensive set of first principle calculations computed from density functional theory. We use the force field to study the adhesion of a single layer MoTe2 on a Cu(111) surface and the results are in good agreement with density functional theory, even though such structures were not part of the training set. We characterized the mobility of the Cu ions intercalated into MoTe2 under the presence of an external electric fields via molecular dynamics simulations. The results show a significant increase in drift velocity for electric fields of approximately 0.4 V/A and that mobility increases with Cu ion concentration.Comment: 21 pages, 9 Figure

Spatially and Temporally Explicit Energy System Modelling to Support the Transition to a Low Carbon Energy Infrastructure – Case Study for Wind Energy in the UK

Renewable energy sources and electricity demand vary with time and space and the energy system is constrained by the location of the current infrastructure in place. The transitioning to a low carbon energy society can be facilitated by combining long term planning of infrastructure with taking spatial and temporal characteristics of the energy system into account. There is a lack of studies addressing this systemic view. We soft-link two models in order to analyse long term investment decisions in generation, transmission and storage capacities and the effects of short-term fluctuation of renewable supply: The national energy system model UKTM (UK TIMES model) and a dispatch model. The modelling approach combines the benefits of two models: an energy system model to analyse decarbonisation pathways and a power dispatch model that can evaluate the technical feasibility of those pathways and the impact of intermittent renewable energy sources on the power market. Results give us the technical feasibility of the UKTM solution from 2010 until 2050. This allows us to determine lower bounds of flexible elements and feeding them back in an iterative process (e.g. storage, demand side control, balancing). We apply the methodology to study the long-term investments of wind infrastructure in the United Kingdom

An expert elicitation of climate, energy and economic uncertainties

Critical energy policy decisions rely on expert assessments of key future uncertainties. But existing modelling techniques that help form these expert assessments often ignore the existence of uncertainty. Consequently, techniques to measure these uncertainties are of increasing importance. We use one technique, expert elicitation, to assess six key uncertain parameters with 25 UK energy experts across academia, government and industry. We obtain qualitative descriptions of the uncertain parameters and a novel data set of probability distributions describing individual expert beliefs. We conduct a sensitivity analysis on weights for a linear opinion pool and show that aggregated median beliefs in 2030 are: for oil price $120/barrel (90$34/tCO2e (90% CI: 5, 256) and for levelised cost of low-carbon electricity 17.1 US cents/kWh (90% CI: 8.3, 31.0). The quantitative results could inform model validation, help benchmark policy makers’ beliefs or provide probabilistic inputs to models

Modelling energy transitions for climate targets under landscape and actor inertia

The speed at which established socioeconomic and technological systems can be adapted to alternatives that are compatible with a climate stabilised, 2. C world remains unknown. Quantitative models used for assessing this challenge typically make a number of arguably optimistic assumptions regarding human behaviour and decision making. This often restricts the insights produced to futures approximating a so-called . first-best policy landscape. However, empirical studies of socio-technical change have shown that technological diffusion is often influenced by actors and institutions interacting under less ideal, . second-best conditions. This paper quantifies these factors in a formal energy model as . landscape and actor inertia and employs them for the first time in BLUE, a dynamic stochastic socio-technical simulation of technology diffusion, energy and emissions inspired by the multi-level perspective. Using the UK energy system as an example, the results illustrate how socio-technical inertia may significantly blunt future efforts to achieve climate targets

The critical role of the industrial sector in reaching long-term emission reduction, energy efficiency and renewable targets

This paper evaluates the critical contribution of the industry sector to long-term decarbonisation, efficiency and renewable energy policy targets. Its methodological novelty is the incorporation of a process-oriented modelling approach based on a comprehensive technology database for the industry sector in a national energy system model for the UK (UKTM), allowing quantification of the role of both decarbonisation of upstream energy vectors and of mitigation options in the industrial sub-categories. This enhanced model is then applied in a comparative policy scenario analysis that explores various target dimensions on emission mitigation, renewable energy and energy efficiency at both a national and European level. The results show that ambitious emission cuts in the industry sector of up to 77% until 2050 compared to 2010 can be achieved. Moreover, with a reduction in industrial energy demand of up to 31% between 2010 and 2050, the sector is essential for achieving the overall efficiency commitments. The industry sector also makes a moderate contribution to the expansion of renewable energies mostly through the use of biomass for low-temperature heating services. However, additional sub-targets on renewable sources and energy efficiency need to be assessed critically, as they can significantly distort the cost-efficiency of the long-term mitigation pathway

The impact of heterogeneous market players with bounded-rationality on the electricity sector low-carbon transition

The energy sector transition requires large financial investments in low-carbon generation technologies, to be delivered by a variety of actors with heterogeneous characteristics. Real-world actors have bounded-rationality, reflected by their limited foresight and heterogeneous expectations, and as past trends influence their investments. Agent-based models are highly suitable modelling frameworks to study such realistic and complex energy transition dynamics. This paper introduces BRAIN-Energy, a novel agent-based model which explicitly allows to explore the impacts of actors' heterogeneous characteristics, and of their interactions, on the transition pathways of the UK, German and Italian electricity sectors. Results show that actors' heterogeneous characteristics pose barriers to effective decarbonisation efforts, affect the speed of the transition, and impact the transition's security of supply and affordability dimensions. Limited foresight and path-dependency lead to investment cycles (both virtuous and vicious). The country comparison highlights how such effects are stronger in markets with more heterogeneous market players

The co-evolution of climate policy and investments in electricity markets: Simulating agent dynamics in UK, German and Italian electricity sectors

Achieving electricity sector transitions consistent with stringent climate change mitigation under the Paris Agreement requires a careful understanding both of the coordinating role of national governments and of its interactions with the heterogeneous market players who will make the low-carbon investments in the electricity sector. However, traditional energy models and scenarios generally assume exogenous policy targets and fail to capture this co-evolution between policy-makers and heterogeneous private and public investors. This paper uses BRAIN-Energy, a novel agent-based model of investment in electricity generation to simulate and contrast government and investor dynamics in the transition pathways of the UK, German and Italian electricity sectors. Key findings show that a successful transition – which achieves the energy policy “trilemma” (low carbon, secure, affordable) – requires the co-evolution of the policy dimension (strong and frequently updatable CO2 price, renewable subsidies and capacity market) with the strategies of the heterogeneous market players. If this dynamic balance is maintained then incentives are politically feasible and suppliers learn and evolve (in what we term a virtuous cycle). If either the incentives are too weak to drive learning or too expensive so the policy regime collapses, then the transition fails on one of its key dimensions (in what we term a vicious cycle). Getting this balance right is harder in risky markets that also have players with more pronounced bounded rationality and path dependence in how they make investments

The key role of historic path-dependency and competitor imitation on the electricity sector low-carbon transition

Market players in the energy sector transition are heterogeneous, have bounded rationality and are influenced by their own past failures, as well as imitating the successes of their competitors. However this agent heterogeneity and complex behaviour in investment choices is not taken into account in traditional energy-economy models used to inform energy sector policies. By using BRAIN-Energy, an agent-based model of investment in electricity generation, which enables to study the impact of actors’ heterogeneous characteristics on the transition pathways of the UK, German and Italian electricity sectors, this paper shows how historic path-dependency in investment choices displaces low-carbon in favour of high-carbon investments under a weak regulatory framework. By contrast, imitation can help the diffusion of renewable technologies, through a self-reinforcing positive feedback when government subsidies to low-carbon investments are in place

An integrated systematic analysis of uncertainties in UK energy transition pathways

Policy goals to transition national energy systems to meet decarbonisation and security goals must contend with multiple overlapping uncertainties. These uncertainties are pervasive through the complex nature of the system, the long term consequences of decisions, and in the models and analytical approaches used. These greatly increase the challenges of informing robust decision making. Energy system studies have tended not to address uncertainty in a systematic manner, relying on simple scenario or sensitivity analysis. This paper utilises an innovative UK energy system model, ESME, which characterises multiple uncertainties via probability distributions and propagates these uncertainties to explore trade-offs in cost effective energy transition scenarios. A linked global sensitivity analysis is used to explore the uncertainties that have most impact on the transition. The analysis highlights the strong impact of uncertainty on delivering the required emission reductions, and the need for an appropriate carbon price. Biomass availability, gas prices and nuclear capital costs emerge as critical uncertainties in delivering emission reductions. Further developing this approach for policy requires an iterative process to ensure a complete understanding and representation of different uncertainties in meeting mitigation policy objectives

An integrated systematic analysis of uncertainties in UK energy transition pathways

Policy goals to transition national energy systems to meet decarbonisation and security goals must contend with multiple overlapping uncertainties. These uncertainties are pervasive through the complex nature of the system, the long term consequences of decisions, and in the models and analytical approaches used. These greatly increase the challenges of informing robust decision making. Energy system studies have tended not to address uncertainty in a systematic manner, relying on simple scenario or sensitivity analysis. This paper utilises an innovative UK energy system model, ESME, which characterises multiple uncertainties via probability distributions and propagates these uncertainties to explore trade-offs in cost effective energy transition scenarios. A linked global sensitivity analysis is used to explore the uncertainties that have most impact on the transition. The analysis highlights the strong impact of uncertainty on delivering the required emission reductions, and the need for an appropriate carbon price. Biomass availability, gas prices and nuclear capital costs emerge as critical uncertainties in delivering emission reductions. Further developing this approach for policy requires an iterative process to ensure a complete understanding and representation of different uncertainties in meeting mitigation policy objectives