Leonardo Barreto

The hydrogen economy in the 21st century: A sustainable development scenario

A long-term hydrogen-based scenario of the global energy system is described in qualitative and quantitative terms here, illustrating the key role of hydrogen in a long-term transition toward a clean and sustainable energy future. In an affluent, low-population-growth, equity and sustainability-oriented B1-H2 world, hydrogen technologies experience substantial but plausible performance and costs improvements and are able to diffuse extensively. Corresponding production and distribution infrastructures emerge. The global hydrogen production system, initially fossil based, progressively shifts toward renewable sources. Fuel cells and other hydrogen-using technologies play a major role in a substantial transformation toward a more flexible, less vulnerable, distributed energy system which meets energy needs in a cleaner, more efficient and cost-effective way. This profound structural transformation of the global energy system brings substantial improvements in energy intensity and security of supply and results in an accelerated decarbonization of the energy mix, with subsequent relatively low climate impacts. Such energy-system path might still not be sufficient to protect against the risk of high climate sensitivities, but hydrogen-based technologies emerge as flexible options for the energy system and, thus, would be prime candidates for a risk management strategy against an uncertain climate future.

Endogenizing R&D and Market Experience in the "Bottom-Up" Energy-Systems ERIS Model

ERIS, an energy-systems optimization model that endogenizes learning curves, is modified in order to incorporate the effects of R&D investments, an important contributing factor to the technological progress of a given technology. For such purpose a modified version of the standard learning curve formulation is applied, where the investment costs of the technologies depend both on cumulative capacity and the so-called knowledge stock. The knowledge stock is a function of R&D expenditures that takes into account depreciation and lags in the knowledge accumulated through R&D. An endogenous specification of the R&D expenditures per technology allows the model to perform an optimal allocation of R&D funds among competing technologies. The formulation is described, illustrative results presented, some insights are derived, and further research needs are identified.

Emissions trading and technology deployment in an energy-systems “bottom-up” model with technology learning

Abstract An important criterion in the analysis of climate policy instruments is their ability to stimulate the technological change necessary to enable the long-term shift towards a low-carbon global energy system. In this paper, some effects of emissions trading on technology deployment when technology learning is endogenized are examined with a multi-regional “bottom-up” energy-systems optimization MARKAL model of the global energy system. In this framework, due to the action of spillovers of learning, imposing emission constraints on a given region may affect the technology choice and emissions profiles of other (unconstrained) regions. The effects depend on the geographical scale of the learning process but also on the presence of emissions trading, the regions that join the trade system and their timing for doing so. Incorporating endogenous technology learning and allowing for spillovers across regions appears as an important mechanism for capturing the possibility of induced technological change due to environmental constraints in “bottom-up” models.

ERIS: A model prototype with endogenous technological change

This paper describes the ERIS Model Prototype, developed within the EU-TEEM project as a flexible tool to study different modelling approaches on the endogenisation of technological change in energy optimisation models. The basic formulation and several variants are presented. Some illustrative results and insights obtained with the prototype are discussed and some perspectives for its future development outlined.

Towards fossil-based electricity systems with integrated CO2 capture: Implications of an illustrative long-term technology policy

This paper examines the role of fossil-fired power plants equipped with carbon capture systems in a long-term scenario of the global energy system. Within this framework, the impacts of a technology policy is illustrated that requires over time an increasing fraction of fossil-fired power generation to incorporate carbon capture technologies leading in the long run to a virtually carbon-free electricity sector. We examine the costs and the potential contribution that such a policy could offer in reducing global energy-related carbon dioxide emissions and highlight some of the technologies that may play a key role in doing so. The analysis is carried out with the energy-systems optimization model MESSAGE considering endogenous technological learning for carbon capture technologies, such that they experience cost reductions as a function of accumulated capacity installations. In the context of a world where fossil-based power systems face pressure to evolve into cleaner configurations in the long term, coal- fired Integrated Gasification Combined-Cycle (IGCC) plants and gas-fired Combined-Cycle (NGCC) plants emerge as flexible, complementary technology choices that, while being attractive for electricity generation, could allow an efficient and cost-effective capture of carbon.

Multi-regional technological learning in the energysystems MARKAL model

This paper describes the implementation of multi-regional endogenous technological learning in the energy optimisation MARKAL model. A mapping procedure is implemented to group learning technologies inside one region or across several regions in a flexible way, in order to allow them to learn together. The approach is described and an illustrative example examining the response of a multi-regional global electricity generation system is presented. The multi-regional learning framework allows the examination of the spatial interactions and mechanisms that affect the technological learning processes in global energy systems. The mutual interactions between the learning and emission trading mechanisms are highlighted. Although emphasis is given to energy modelling, some policy insights can be gained. The results highlight the importance of fostering international cooperation to stimulate the learning process of emerging energy technologies.

Flexible Carbon Mitigation Policies: Analysis with a Global Multi-Regional MARKAL Model

The Swiss National Centre of Competence in Research (NCCR-Climate) explores the predictability, variability and risks of climate change and the socio-economic response to it. The Paul Scherrer Institut (PSI) and the University of Geneva contribute to this programme by using models to simulate the impacts of policies for climate change mitigation. This study quantifies the benefits of several policies enhancing the flexibility of carbon dioxide (CO2) mitigation, with emphasis placed on emissions trading, optimal timing paths and support for learning-by-doing (LBD) in the use of low-carbon technologies. We present illustrative results for a “Soft-landing” scenario, which imposes a CO2-emission stabilization target that is consistent with stabilizing CO2concentration at 550 ppmv in the long run. This analysis has been conducted with the Global MARKAL Model (GMM), which is a multi-regional, “bottom-up”, partial equilibrium energy-system model with endogenized technology learning (ETL). Incorporation of flexible CO2mitigation policies leads to significant reductions in energy-system costs and marginal costs of CO2abatement as well as increasing diffusion of advanced low-carbon technologies. In the future, an extended GMM model could be linked to a climate model (e.g., C-Goldstein, Marsh et al., 2002) to implement an Integrated Assessment Model (IAM) that would allow examining impacts of climate change.

A post-Kyoto analysis with the ERIS model prototype

Electricity generation technologies are examined in a global context with a multi-regional version of the ERIS model prototype with endogenous technological learning curves, developed within the EU/TEEM project. Impacts of Kyoto-like CO2 constraints are analysed considering the effects of allowing or not trade of emission permits. Complementary stochastic analyses addressing the uncertainty of emission constraints, demand and learning rates and a preliminary assessment of the effects of the geographical scale of learning are also presented. When technology dynamics are endogenous, mitigation policies stimulate technological learning of emerging marginal low carbon technologies driving the model to their early deployment. Trade of emission permits allows some of the constrained regions to take more moderate actions, but provides opportunities for penetration of learning technologies in different regions, contributing to their long term cost competitiveness. Early action appears to be effective in terms of long term costs and emission profiles. Uncertainties in emission targets and demands may stimulate technological learning as a preparation for future contingencies.

Supporting hydrogen based transportation: case studies with Global MARKAL Model

Steadily growing prices of oil and emissions coming from conventional vehicles, might force a switch to an alternative and less polluting fuel in the coming future. In this article we analyze the potential influence of selected factors for successful market penetration of hydrogen fuel cell vehicles in hydrogen based private transportation economy. Using a world scale, full energy system, bottom-up, optimization model (Global MARKAL Model—GMM) we address the possibility of supporting the fuel cell vehicle technology to become competitive in the markets. In a series of optimizations we evaluate the potential influence of governmental supports and the internalization of externalities related to CO2 and local pollution emissions originating from the transportation sector, as well as preferential crediting options and demonstration projects promoting fuel cell vehicles. The results suggest that the crucial element is the price of fuel cells and their further potential to reduce costs. This reduction of costs may be triggered by governmental support such as direct subsidies to fuel cells, preferential crediting options for the buildup of hydrogen infrastructure as well as penalization of emitters of CO2 and/or local pollutants.

The Effect of Government Actions on Environmental Technology Innovation: Applications to the Integrated Assessment of Carbon Sequestration Technologies

This project seeks to improve the ability of integrated assessment models (IA) to incorporate changes in technology, especially environmental technologies, cost and performance over time. In this report, we present results of research that examines past experience in controlling other major power plant emissions that might serve as a reasonable guide to future rates of technological progress in carbon capture and sequestration (CCS) systems. In particular, we focus on U.S. and worldwide experience with sulfur dioxide (SO{sub 2}) and nitrogen oxide (NO{sub x}) control technologies over the past 30 years, and derive empirical learning rates for these technologies. The patterns of technology innovation are captured by our analysis of patent activities and trends of cost reduction over time. Overall, we found learning rates of 11% for the capital costs of flue gas desulfurization (FGD) system for SO{sub 2} control, and 13% for selective catalytic reduction (SCR) systems for NO{sub x} control. We explore the key factors responsible for the observed trends, especially the development of regulatory policies for SO{sub 2} and NO{sub x} control, and their implications for environmental control technology innovation.