Uncertainty in the learning rates of energy technologies: An experiment in a global multi-regional energy system model

The diffusion of promising energy technologies in the market depends on their future energy production–cost development. When analyzing these technologies in an integrated assessment model using endogenous technological learning, the uncertainty in the assumed learning rates (LRs) plays a crucial role in the production–cost development and model outcomes. This study examines the uncertainty in LRs of some energy technologies under endogenous global learning implementation and presents a floor-cost modeling procedure to systematically regulate the uncertainty in LRs of energy technologies. The article narrates the difficulties of data assimilation, as compatible with mixed integer programming segmentations, and comprehensively presents the causes of uncertainty in LRs. This work is executed using a multi-regional and long-horizon energy system model based on “TIMES” framework. All regions receive an economic advantage to learn in a common domain, and resource-ample regions obtain a marginal advantage for better exploitation of the learning technologies, due to a lower supply-side fuel-cost development. The lowest learning investment associated with the maximum LR mobilizes more deployment of the learning technologies. The uncertainty in LRs has an impact on the diffusion of energy technologies tested, and therefore this study scrutinizes the role of policy support for some of the technologies investigated.     

Global technology learning and national policy—An incentive scheme for governments to assume the high cost of early deployment exemplified by Norway

In this paper it is argued that technology learning may be both a barrier and an incentive for technology change in the national energy system. The possibility to realize an ambitious global emission reduction scenario is enhanced by coordinated action between countries in national policy implementation. An indicator for coordinated action is suggested. Targeted measures to increase deployment of nascent energy technologies and increasing energy efficiency in a small open economy like Norway are examined. The measures are evaluated against a set of baselines with different levels of spillover of technology learning from the global market. It is found that implementation of technology subsidies increase the national contribution to early deployment independent of the level of spillover. In a special case with no spillover for offshore floating wind power and endogenous technology learning substantial subsidy or a learning rate of 20% is required. Combining the high learning rate and a national subsidy increases the contribution to early deployment. Enhanced building code on the other hand may reduce Norway’s contribution to early deployment, and thus the realization of a global emission reduction scenario, unless sufficient electricity export capacity is assured.     

Endogenous implementation of technology gap in energy optimization models—a systematic analysis within TIMES G5 model

Evaluation of global diffusion potential of learning technologies and their timely specific cost development across regions is always a challenging issue for the future technology policy preparation. Further the process of evaluation gains interest especially by endogenous treatment of energy technologies under uncertainty in learning rates with technology gap across the regions in global regional cluster learning approach. This work devised, implemented, and examined new methodologies on technology gaps (a practical problem), using two broad concepts of knowledge deficit and time lag approaches in global learning, applying the floor cost approach methodology. The study was executed in a multi-regional, technology-rich and long horizon bottom-up linear energy system model on The Integrated MARKAL EFOM System (TIMES) framework. Global learning selects highest learning technologies in maximum uncertainty of learning rate scenario, whereas any form of technology gap retards the global learning process and discourages the technologies deployment. Time lag notions of technology gaps prefer heavy utilization of learning technologies in developed economies for early reduction of specific cost. Technology gaps of any kind should be reduced among economies through the promotion and enactment of various policies by governments, in order to utilize the technological resources by mass deployment to combat ongoing climate change.     

Implications of technological learning on the prospects for renewable energy technologies in Europe

The objective of this article is to examine the consequences of technological developments on the market diffusion of different renewable electricity technologies in the EU-25 until 2020, using a market simulation model (ADMIRE REBUS). It is assumed that from 2012 a harmonized trading system will be implemented, and a target of 24% renewable electricity (RES-E) in 2020 is set and met. By comparing optimistic and pessimistic endogenous technological learning scenarios, it is found that diffusion of onshore wind energy is relatively robust, regardless of technological development, but diffusion rates of offshore wind energy and biomass gasification greatly depend on their technological development. Competition between these two options and (existing) biomass combustion options largely determines the overall costs of electricity from renewables and the choice of technologies for the individual member countries. In the optimistic scenario, in 2020 the market price for RES-E is 1 €ct/kWh lower than in the pessimistic scenario (about 7 vs. 8 €ct/kWh). As a result, total RES-E production costs are 19% lower, and total governmental expenditures for RES-market stimulation are 30% lower in the optimistic scenario.     

Technology learning for renewable energy: Implications for South Africa's long-term mitigation scenarios

Technology learning can make a significant difference to renewable energy as a mitigation option in South Africa's electricity sector. This article considers scenarios implemented in a Markal energy model used for mitigation analysis. It outlines the empirical evidence that unit costs of renewable energy technologies decline, considers the theoretical background and how this can be implemented in modeling. Two scenarios are modelled, assuming 27% and 50% of renewable electricity by 2050, respectively. The results show a dramatic shift in the mitigation costs. In the less ambitious scenario, instead of imposing a cost of Rand 52/t CO₂-eq (at 10% discount rate), reduced costs due to technology learning turn renewables into negative cost option. Our results show that technology learning flips the costs, saving R143. At higher penetration rate, the incremental costs added beyond the base case decline from R92 per ton to R3. Including assumptions about technology learning turns renewable from a higher-cost mitigation option to one close to zero. We conclude that a future world in which global investment in renewables drives down unit costs makes it a much more cost-effective and sustainable mitigation option in South Africa.     

Renewable energy technologies for irrigation water pumping in India: projected levels of dissemination, energy delivery and investment requirements using available diffusion models

Using the past diffusion trends of four renewable energy technologies for irrigation water pumping in India (SPV pumps, windmill pumps and biogas/producer gas driven dual fuel engine pumps), results of an attempt to project their future dissemination levels, have been presented in this study. The likely contribution of the renewable energy options considered in the study to the projected energy demand for irrigation water pumping in India has been estimated. Estimates of the associated investment requirements taking into account the learning effect have also been presented.     

Useful models for simulating policies to induce technological change

Conventional top-down and bottom-up energy–economy models have limitations that affect their usefulness to policy-makers. Efforts to develop hybrid models, that incorporate valuable aspects of these two frameworks, may be more useful by representing technologies in the energy–economy explicitly while also representing more realistically the way in which businesses and consumers choose between those technologies. This representation allows for the realistic simulation of a wide range of technology-specific regulations and fiscal incentives alongside economy-wide fiscal incentives and disincentives. These policies can be assessed based on the costs required to reach a goal in the medium term, as well as on the degree to which they induce technological change that affects costs over long time periods.     

Modeling technological learning and its application for clean coal technologies in Japan

Estimating technological progress of emerging technologies such as renewables and clean coal technologies becomes important for designing low carbon energy systems in future and drawing effective energy policies. Learning curve is an analytical approach for describing the decline rate of cost and production caused by technological progress as well as learning. In the study, a bottom-up energy-economic model including an endogenous technological learning function has been designed. The model deals with technological learning in energy conversion technologies and its spillover effect. It is applied as a feasibility study of clean coal technologies such as IGCC (Integrated Coal Gasification Combined Cycle) and IGFC (Integrated Coal Gasification Fuel Cell System) in Japan. As the results of analysis, it is found that technological progress by learning has a positive impact on the penetration of clean coal technologies in the electricity market, and the learning model has a potential for assessing upcoming technologies in future.     

The resilience of the Indian economy to rising oil prices as a validation test for a global energy–environment–economy CGE model

This paper proposes to test the global hybrid computable general equilibrium model Imaclim-R against macroeconomic data. To do so, it compares the modeled and observed responses of the Indian economy to the rise of oil price during the 2003–2006 period. The objective is twofold: first, to disentangle the various mechanisms and policies at play in India's economy response to rising oil prices and, second, to validate our model as a tool capable of reproducing short-run statistical data. With default parameterization, the model predicts a significant decrease in the Indian growth rate that is not observed. However, this discrepancy is corrected if three additional mechanisms identified by the International Monetary Fund are introduced, namely the rise in exports of refined oil products, the imbalance of the trade balance allowed by large capital inflows, and the incomplete pass-through of the oil price increase to Indian customers. This work is a first step toward model validation, and provides interesting insights on the modeling methodology relevant to represent an economy's response to a shock, as well as on how short-term mechanisms – and policy action – can smooth the negative impacts of energy price shocks or climate policies.     

Incorporating carbon capture and storage technologies in integrated assessment models

Low-carbon emitting technologies are a key component of technical change in integrated assessment models. We develop a methodology for incorporating technologies into computable general equilibrium economic models and demonstrate this methodology by implementing carbon capture and storage technologies in the MIT Emissions Prediction and Policy Analysis (EPPA) model. Three primary implementation issues are discussed: characterization of the technical system, translation of bottom–up engineering information into an economic model, and the depiction of realistic technology adoption rates. The specification of input substitution, relative costs, and plant dispatch are the most critical factors in technology representation. Technology adoption rates in economic models are governed by exogenous and endogenous constraints. A comparison of the current approaches used in economic models with the theoretical and empirical factors affecting adoption rates highlights opportunities for refining the current methods.     

Discount rate for technology assessment: An application to the energy sector

When comparing the relative merits of technologies in the energy sector, analysis of a long time span is frequently required. Discounting of costs and benefits is usual in such analysis, and the choice of an appropriate discount rate can be vital. The available empirical evidence suggests that the appropriate real discount rate could vary widely depending on which concept it is based. For Australia, a value reflecting the social time preference rate could be as low as 2%, while one based on the social opportunity cost of capital would be considerably higher, probably 7–10%. A multiperiod linear programming model, MARKAL, suitable for energy technology assessment, was used to analyse the effect on the optimal long-term energy strategy for Australia, of using discount rates over the range 2–10%. The results indicate that capital-intensive technologies such as coal liquefaction and solar water heaters are disadvantaged when using high discount rates. This could result in energy strategies being pursued in which the level of oil imports is much     

Exploring past energy changes and their implications for the pace of penetration of new energy technologies

Possible growth paths for new electricity generation technologies are investigated on the basis of an empirical analysis of past penetration rates. Finding and understanding high market penetration scenarios is relevant to formulating climate change mitigation strategies. The analysis shows that under favorable growth conditions, photovoltaics and wind could produce 15% and 25%, respectively, of world electricity by 2050. Under the same assumptions nuclear power could increase to 41% of world electricity. But it is unlikely that all three technology paths could be realized up to these values simultaneously and therefore the penetration rates presented here should be considered as indicative only. The results show that under positive conditions, an embryonic technology could move as a preferred option into a mainstream energy source within half a century. The introduction of growth constraints reflecting, e.g., severe economic, technical, or political limitations could reduce the above numbers by a factor of up to 2–3. The results indicate a decline in the relative year-to-year growth of new technologies when they have higher market shares. A comparison of the results with other short-term and long-term technology scenarios shows satisfactory agreement.     

Interactions of energy technology development and new energy exploitation with water technology development in China

Interactions of energy policies with water technology development in China are investigated using a hybrid input-output model and scenario analysis. The implementation of energy policies and water technology development can produce co-benefits for each other. Water saving potential of energy technology development is much larger than that of new energy exploitation. From the viewpoint of proportions of water saving co-benefits of energy policies, energy sectors benefit the most. From the viewpoint of proportions of energy saving and CO₂ mitigation co-benefits of water technology development, water sector benefits the most. Moreover, economic sectors are classified into four categories concerning co-benefits on water saving, energy saving and CO₂ mitigation. Sectors in categories 1 and 2 have big direct co-benefits. Thus, they can take additional responsibility for water and energy saving and CO₂ mitigation. If China implements life cycle materials management, sectors in category 3 can also take additional responsibility for water and energy saving and CO₂ mitigation. Sectors in category 4 have few co-benefits from both direct and accumulative perspectives. Thus, putting additional responsibility on sectors in category 4 might produce pressure for their economic development.     

Decarbonising road transport with hydrogen and electricity: Long term global technology learning scenarios

Both fuel cell and electric vehicles have the potential to play a major role in a transformation towards a low carbon transport system that meets travel demands in a cleaner and more efficient way if hydrogen and electricity was produced in a sustainable manner. Cost reductions are central to this challenge, since these technologies are currently too expensive to compete with conventional vehicles based on fossil fuels. One important mechanism through which technology costs fall is learning-by-doing, the process by which cumulative global deployment leads to cost reduction. This paper develops long-term scenarios by implementing global technology learning endogenously in the TIAM-UCL global energy system model to analyse the role of hydrogen and electricity to decarbonise the transport sector. The analysis uses a multi-cluster global technology learning approach where key components (fuel cell, electric battery and electric drive train), to which learning is applied, are shared across different vehicle technologies such as hybrid, plug-in hybrid, fuel cell and battery operated vehicles in cars, light goods vehicles and buses. The analysis shows that hydrogen and electricity can play a critical role to decarbonise the transport sector. They emerge as complementary transport fuels, rather than as strict competitors, in the short and medium term, with both deployed as fuels in all scenarios. However, in the very long-term when the transport sector has been almost completely decarbonised, technology competition between hydrogen and electricity does arise, in the sense that scenarios using more hydrogen in the transport sector use less electricity and vice versa.     

Heterogeneous technologies,strategic groups and environmental efficiency technology gaps for European countries

This paper measures technology (TG) and environmental efficiency technology gaps (EETGs) in 25 European countries over two distinct periods 2002 and 2008 examining the possible effect of adopted environmental regulations and the Kyoto protocol commitments on environmental efficiency technology gaps. However, the introduction of the metafrontier in our analysis puts into our discussion the role of heterogeneous technologies and its effect on the above-mentioned measures. Employing a directional distance function, we investigate whether there is an actual difference, in terms of environmental efficiency and efficiency performance, among European countries considering the technological frontiers under which they operate. The construction of individual frontiers has been realized employing a large number of variables that are highly correlated with countries' learning and absorbing capacity, new technological knowledge and using economic theory and classical frontier discrimination like developed vs. developing, North vs. South and participation in the Eurozone or not. The overall results indicate a crucial role of heterogeneous technologies for technology gaps in both periods. Moreover, a significant decrease for both measures, although in different percent, has been recorded emphasizing the key role of knowledge spillovers.     

全球新能源技术发展:以技术垄断与技术扩散为视角

概述了全球新能源技术发展格局,以技术垄断和技术扩散为视角分析了新能源技术垄断与技术扩散的形成机理与关系,介绍了发达国家新能源技术垄断与技术扩散的模式,并指出未来我国新能源技术发展的两条路径.     

A global model for residential energy use: Uncertainty in calibration to regional data

Uncertainties in energy demand modelling allow for the development of different models, but also leave room for different calibrations of a single model. We apply an automated model calibration procedure to analyse calibration uncertainty of residential sector energy use modelling in the TIMER 2.0 global energy model. This model simulates energy use on the basis of changes in useful energy intensity, technology development (AEEI) and price responses (PIEEI). We find that different implementations of these factors yield behavioural model results. Model calibration uncertainty is identified as influential source for variation in future projections: amounting 30% to 100% around the best estimate. Energy modellers should systematically account for this and communicate calibration uncertainty ranges.     

Soft-linking energy systems and GIS models to investigate spatial hydrogen infrastructure development in a low-carbon UK energy system

This paper describes an innovative modelling approach focusing on linking spatial (GIS) modelling of hydrogen (H₂) supply, demands and infrastructures, anchored within a economy-wide energy systems model (MARKAL). The UK government is legislating a groundbreaking climate change mitigation target for a 60% CO₂ reduction by 2050, and has identified H₂ infrastructures and technologies as potentially playing a major role, notably in the transport sector. An exploratory set of linked GIS–MARKAL model scenarios generate a range of nuanced insights including spatial matching of supply and demand for optimal zero-carbon H₂ deployment, a crucial finding on successive clustering of demand centres to enable economies of scale in H₂ supply and distribution, the competitiveness of imported liquid H₂ and of liquid H₂ distribution, and sectoral competition for coal with carbon sequestration between electricity and H₂ production under economy-wide CO₂ constraints.     

A fuzzy analytic hierarchy process approach for assessing national competitiveness in the hydrogen technology sector

As it is more environmentally sound and friendly than conventional energy technologies that emit carbon dioxide, hydrogen technology can play a key role in solving the problems caused by the greenhouse gas effect and in coping with the hydrogen economy. Numerous countries around the world, including Korea, have increasingly focused on R&D where hydrogen technology development is concerned. This paper focuses on the use of the fuzzy analytic hierarchy process (fuzzy AHP), which is an extension of the AHP method and uses interval values to reflect the vagueness of human thought, to assess national competitiveness in the hydrogen technology sector. This analysis based on the AHP and fuzzy AHP methods revealed that Korea ranked 6th in terms of national competitiveness in the hydrogen technology sector.     

An energy demand model for a fleet of plug-in fuel cell vehicles and commercial building interfaced with a clean energy hub

This paper presents an energy demand model for a fleet of plug-in fuel cell vehicles and a medium-sized commercial/office building interfaced with a clean energy hub. The approach taken is to model the architecture and daily operation of every individual vehicle in the fleet. A simplified architecture model was developed, with daily operation divided into two periods: charging and travelling. During the charging period, the vehicle charges its batteries and refills its compressed hydrogen tanks. During the travelling period, the vehicle depletes the batteries and hydrogen tanks based on distance travelled. Daily travel distance is generated by a stochastic model. The modeling of the clean energy hub is also presented. The clean energy hub functions as an interface between electricity supply and the energy demand (i.e. hydrogen and electricity) of the vehicle fleet and the commercial building. Finally, a sample case is presented to demonstrate the use of the models.