Long-term Energy Model Research Articles

A baseline long term technoeconomic electricity supply model for highly regulated fuel importing countries prone to shortages: Case of Jordan

Jordan, like other fuel-importing countries, is highly susceptible to fuel shortages and price fluctuations due to global events. Political turmoil in the region has frequently disrupted Jordan's energy supply, including the recent cessation of natural gas imports from Egypt, which is examined in this study. To address these interruptions, Jordan signed a 15-year contract to import natural gas from the Leviathan gas field. Jordan has long term power purchase agreements with independent power producers. thus, creating a safe investment environment in the electricity production sector. In this research a long-term energy model was developed and implemented for Jordan with the Open-Source long-term energy modeling tool OSeMOSYS. The model is used to assess current policy and compare options moving forward. This study brings into focus the issues facing fuel importing countries prone to shortages. Contractual aspects were incorporated in the model and investigated. The need to diversify natural gas import contracts with relatively shorter durations was demonstrated. The impact of different fuel pricing projections was highlighted and needs to be considered when drawing up such contracts. It was also demonstrated that the current renewable energy outlook needs modification. potential of renewable energy needs added consideration from stakeholders and policy makers.

Strategies for short-term intermittency in long-term prospective scenarios in the French power system

This paper depicts the power system adequacy with respect to nuclear strategies by coupling investment with dispatching. The long-term energy model POLES simulates the Paris Agreement worldwide and is soft-linked with a power market model applied to France, EcoNUK. The nuclear flexibility is described by cycling frequency and amplitude, constrained by reactors minimum rated power and half-hour ramping rates. Results in 2050 show that the French power system made of 26% nuclear and 71% renewables in POLES needs deeper and longer flexibility with nuclear and gas in EcoNUK, due mainly to higher granular time-steps than the prospective model; and that reactors perform more deep cycles than allowed by their license (230 instead of 200). We show that scenarios with high shares of renewables build on the arbitrage between nuclear and gas, notably during peak loads in winter and night periods. Meeting the double target to reduce nuclear and carbon emissions requires more renewables, hence significant gas and nuclear power for adequacy, facing the dilemma nuclear versus emissions. Coupling short-term operation with long-term investment indicates that nuclear flexibility varies with the time-step of intermittency modeling, so scenarios need to include reactors constraints to reach an informed decision on renewables and nuclear.

Improving material projections in Integrated Assessment Models: The use of a stock-based versus a flow-based approach for the iron and steel industry

The steel industry is responsible for a large share of the industrial energy consumption and greenhouse gas emissions and several long-term energy models have some representation of this sub-sector. It is found that models, commonly use a flow-based approach for projecting steel demand neglecting that in-use steel stocks serve as a better demand indicator than steel consumption. A stock-based method that uses the historical steel stock results from detailed material flow analysis is developed for making steel demand projections and implemented in the IMAGE Integrated Assessment Model. Large differences between the two approaches arise. For the first half of the 21st century, global steel demand increases with both approaches and at a similar rate to reach 2300 Mt/yr by 2050. For the second half of the 21st century, however, the developments differ drastically. With the stock-based approach, global steel demand decreases by 0.8%/a to reach 1600 Mt/yr, while with the flow-based approach it increases by 0.3%/a to reach 2600 Mt/yr in 2100. Given that steel production levels have a profound contribution to greenhouse gas emissions, using the right approach is crucial. This means that long-term energy models may currently overestimate the industrial emissions in the last half of the century.

Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results

Variable electricity generation from wind and solar influences the design of a cost-efficient and reliable energy system. This paper presents a method that uses stochastic programming to represent variable renewable electricity generation in long-term energy system models, and demonstrates this on a Norwegian TIMES model. First, we derive hourly PV- and wind-generation data by modifying satellite-based data, based on a comparison with historical generation data. Second, the satellite-based dataset is transformed into a manageable set of scenarios that is used as an input to the stochastic energy-system model. This is done using six different scenario generation methods. Third, we solve the energy-system model with three of the scenario-generation methods and evaluate the quality of the corresponding model value by stability tests. We demonstrate that scenarios generated from the six methods have significantly different moment-based and Wasserstein distance error relative to the dataset. Further, the energy system model results show that the number of scenarios needed to achieve stability differs between the three used scenario generation methods.

European transmission grid expansion as a flexibility option in a scenario of large scale variable renewable energies integration

This paper presents a new power sector module, called EUTGRID, which is coupled with the long-term energy model POLES to deliver a suitable framework for considering grid aspects in energy modelling allowing for more distinct analysis of energy technology development and energy policy. It includes a mechanism of investment in transmission grids based on nodal prices together with a DC-load flow and a more detailed description of the European transmission grid. The methodology goes beyond “conventional” energy systems modelling, where the electricity grid is usually represented as a copper plate.

Transitioning remote Arctic settlements to renewable energy systems – A modelling study of Longyearbyen, Svalbard

As transitioning away from fossil fuels to renewable energy sources comes on the agenda for a range of energy systems, energy modelling tools can provide useful insights. If large parts of the energy system turns out to be based on variable renewables, an accurate representation of their short-term variability in such models is crucial. In this paper, we have developed a stochastic long-term energy model and applied it to an isolated Arctic settlement as a challenging and realistic test case. Our findings suggest that the stochastic modelling approach is critical in particular for studies of remote Arctic energy systems. Furthermore, the results from a case study of the Norwegian settlement of Longyearbyen, suggest that transitioning to a system based on renewable energy sources is feasible. We recommend that a solution based mainly on renewable power generation, but also including energy storage, import of hydrogen and adequate back-up capacity is taken into consideration when planning the future of remote Arctic settlements.

The scope for better industry representation in long-term energy models: Modeling the cement industry

Although the cement industry emits around 6% of global CO2 emissions, most global Integrated Assessment Models (IAMs) barely represent this industrial subsector or do not cover all important processes. This study, describes the state-of-the-art of cement modelling in IAMs, suggests possible improvements and discusses the impacts of these on energy and greenhouse gas emissions (GHG) in the IMAGE global IAM.It is found that two cement-sector specific GHG mitigation measures are often not explicitly accounted for in IAMs, namely: (i) retrofitting and (ii) reducing the clinker to cement ratio. For retrofitting, many measures are identified as cost-effective and when incorporating these in the IMAGE model overall energy use reduces between 2010 and 2035 by 9.8 and 11 EJ (4% and 5%) under the baseline and GHG mitigation scenarios, respectively. When incorporating the clinker to cement ratio by linking material availability to the activities in the steel industry and coal-fired power plants, the 2050 energy use reduces by 15% under the baseline scenario and increases by 9% under the GHG mitigation scenario as fewer coal-fired power plants are in operation. This is even more prominent in the long term. The 2100 energy use is 14% higher in the GHG mitigation scenario as even fewer coal-fired power plants are used drastically limiting the potential for clinker substitution with fly ash. These results highlight the importance of capturing cross-sectoral relationships between industries and of including sector specific mitigation measures in long-term energy models.

Energy technology environment model with smart grid and robust nodal electricity prices

This paper deals with the modeling of power flow in a transmission grid within the multi-sectoral multi-energy long-term regional energy model ETEM-SG. This extension of the model allows a better representation of demand response for flexible loads triggered by nodal marginal cost pricing. To keep the global model in the realm of linear programming one uses a linearized DC power flow model that represents the transmission grid with the main constraints on the power flowing through the different arcs of the electricity transmission network. Robust optimization is used to take into account the uncertainty on the capacity limits resulting from inter-regional transit. A numerical illustration is carried out for a data set corresponding roughly to the Leman Arc region.

Comparing projections of industrial energy demand and greenhouse gas emissions in long-term energy models

The industry sector is a major energy consumer and GHG emitter. Effective climate change mitigation strategies will require a significant reduction of industrial emissions. To better understand the variations in the projected industrial pathways for both baseline and mitigation scenarios, we compare key input and structure assumptions used in energy-models in relation to the modeled sectors' mitigation potential. It is shown that although all models show in the short term similar trends in a baseline scenario, where industrial energy demand increases steadily, after 2050 energy demand spans a wide range across the models (between 203 and 451 EJ/yr). In Non-OECD countries, the sectors energy intensity is projected to decline relatively rapidly but in the 2010–2050 period this is offset by economic growth.The ability to switch to alternative fuels to mitigate GHG emissions differs across models with technologically detailed models being less flexible in switching from fossil fuels to electricity. This highlights the importance of understanding economy-wide mitigation responses and costs and is therefore an area for improvements. By looking at the cement sector in more detail, we show that analyzing each industrial sub-sector separately can improve the interpretation and accuracy of outcomes, and provide insights in the feasibility of GHG abatement.

Universal access to electricity in Burkina Faso: scaling-up renewable energy technologies

This paper describes the status quo of the power sector in Burkina Faso, its limitations, and develops a new methodology that through spatial analysis processes with the aim to provide a possible pathway for universal electricity access. Following the SE4All initiative approach, it recommends the more extensive use of distributed renewable energy systems to increase access to electricity on an accelerated timeline. Less than 5% of the rural population in Burkina Faso have currently access to electricity and supply is lacking at many social structures such as schools and hospitals. Energy access achievements in Burkina Faso are still very modest. According to the latest SE4All Global Tracking Framework (2015), the access to electricity annual growth rate in Burkina Faso from 2010 to 2012 is 0%. The rural electrification strategy for Burkina Faso is scattered in several electricity sector development policies: there is a need of defining a concrete action plan. Planning and coordination between grid extension and the off-grid electrification programme is essential to reach a long-term sustainable energy model and prevent high avoidable infrastructure investments. This paper goes into details on the methodology and findings of the developed Geographic Information Systems tool. The aim of the dynamic planning tool is to provide support to the national government and development partners to define an alternative electrification plan. Burkina Faso proves to be paradigm case for the methodology as its national policy for electrification is still dominated by grid extension and the government subsidising fossil fuel electricity production. However, the results of our analysis suggest that the current grid extension is becoming inefficient and unsustainable in order to reach the national energy access targets. The results also suggest that Burkina Faso’s rural electrification strategy should be driven local renewable resources to power distributed mini-grids. We find that this approach would connect more people to power more quickly, and would reduce fossil fuel use that would otherwise be necessary for grid extension options.

Investigating the value of fusion energy using the Global Change Assessment Model

The availability of fusion energy could prove valuable in meeting carbon mitigation targets over the course of the century. We use recent cost estimates for future fusion power plants in order to incorporate fusion into the Global Change Assessment Model (GCAM), a long-term energy and environment model used to study the interaction between technology, climate, and public policy. Results show that fusion's growth will depend on: the chosen carbon mitigation target (if any); the availability of competing carbon-neutral options for the provision of baseload electrical power, in particular nuclear fission as well as carbon capture and storage; the chosen discount rate; the initial year of availability; and the assumed costs of fusion electricity. We quantify the present value of the fusion option while varying the assumptions about these other parameters, and we find that it is, in general for our range of assumptions, significantly larger than the estimated cost of a comprehensive R&D plan to develop fusion energy. The results emphasize the wisdom in hedging against uncertainty in future technology availability by pursuing the development of multiple options that could feasibly play a major role in the latter half of the century.

Supporting security and adequacy in future energy systems: The need to enhance long-term energy system models to better treat issues related to variability

As the shares of variable renewable generation in power systems increase, so does the need for, inter alia, flexible balancing mechanisms. These mechanisms help ensure the reliable operation of the electricity system by compensating for fluctuations in supply or demand. However, a focus on short-term balancing is sometimes neglected when assessing future capacity expansions with long-term energy system models. Developing heuristics that can simulate short-term system issues is one way of augmenting the functionality of such models. To this end, we present an extended functionality to the Open Source Energy Modelling System (OSeMOSYS), which captures the impacts of short-term variability of supply and demand on system adequacy and security. Specifically, we modelled the system adequacy as the share of wind energy is increased. Further, we enable the modelling of operating reserve capacities required for balancing services. The dynamics introduced through these model enhancements are presented in an application case study. This application indicates that introducing short-term constraints in long-term energy models may considerably influence the dispatch of power plants, capacity investments, and, ultimately, the policy recommendations derived from such models.

Worldwide cheap and heavy oil productions: A long-term energy model

Crude oil, natural gas liquids, heavy oils, deepwater oils, and polar oils are non-renewable energy resources with increasing extraction costs. Two major definitions emerge: regular or ‘cheap’ oil and non-conventional or ‘heavy’ oil. Peaking time in conventional oil production has been a recent focus of debate. For two decades, non-conventional oils have been mixed with regular crude oil. Peaking time estimation and the rate at which production may be expected to decline, following the peak, are more difficult to determine. We propose a two-wave model for world oil production pattern and forecasting, based on the diffusion of innovation theories: a sequential multi-Bass model. Historical well-known shocks are confirmed, and new peaking times for crude oil and mixed oil are determined with corresponding depletion rates. In the final section, possible ties between the dynamics of oil extraction and refining capacities are discussed as a predictive symptom of an imminent mixed oil peak in 2016.

The relevance of the energy resource dynamics in the mid/long-term energy planning models

This work analyzes different modeling methodologies for balancing the electricity supply sources and the electricity demand in systems with high penetration of intermittent renewable energy sources, such as wind and run-of-river hydro. This work also explores the reasons and the circumstances where common balance approaches used by mid- and long-term energy models show significant differences in dispatched renewable sources, overestimating the renewable share in the electricity mix and underestimating the amount of CO 2 emitted by the electric system, when compared to balance methods with high time resolution. These reasons and circumstances are illustrated for the Flores island (Azores) case study which has already achieved a share of 50% of renewable energies in electricity production in 2009.

A centurial history of technological change and learning curves for pulverized coal-fired utility boilers

Recent study of the history of technological change has provided better understanding of the driving forces for technological innovation, as well as quantitative estimates of historical rates of technical change. Although such results are widely used in long-term energy models to estimate future costs over time periods of up to a century, most studies of technological learning for major energy technologies are based on historical trends over time periods not longer than 20–30 years (often because of data limitations). Relatively few studies quantify longer-term (century-scale) trends. This study helps fill that gap by reviewing the history of pulverized-coal (PC) power plants, with a specific focus on the technological progress of PC boiler technology over the last century. Historical data for U.S. plants are used to develop long-term experience curves for the overall thermal efficiency of PC power plants, as well as the capital cost of PC boilers and non-fuel operating and maintenance (O&M) costs of PC plants. Despite a technology plateau experienced by PC power plants two decades ago, recent developments indicate that such plants will continue to improve and remain a competitive and important part of power generation technology portfolios.

The potential role of hydrogen in energy systems with and without climate policy

Introduction of hydrogen in global energy system can lead to lower CO 2 emissions (high end-use efficiency; low-carbon production of hydrogen) but might also increase CO 2 emissions (producing hydrogen from coal). We used the long-term energy model TIMER 2.0, to study the use and production of hydrogen and its influence on global CO 2 emissions. This is done using a set of scenarios with assumptions on technology development, infrastructural barriers and climate policy (CP). We found that even under optimistic assumptions hydrogen plays a minor role in the global energy system until the mid-21st century due to the system inertia—but could become a dominant secondary energy carrier in the second half of the century. Hydrogen is mainly produced from coal and natural gas. Hence, hydrogen-rich scenarios without climate policy increase CO 2 emissions up to 15% by 2100 compared to the baseline. However, if climate policy is assumed, CO 2 from fossil feedstock-based hydrogen production is captured and sequestrated, which indicates that an energy system that includes hydrogen is much more flexible in responding to climate policy.

Studies of breakeven prices and electricity supply potentials of nuclear fusion by a long-term world energy and environment model

In response to social demand, this paper investigates the breakeven price (BP) and potential electricity supply of nuclear fusion energy in the 21st century by means of a world energy and environment model. We set the following objectives in this paper: (i) to reveal the economics of the introduction conditions of nuclear fusion; (ii) to know when tokamak-type nuclear fusion reactors are expected to be introduced cost-effectively into future energy systems; (iii) to estimate the share in 2100 of electricity produced by the presently designed reactors that could be economically selected in the year. The model can give in detail the energy and environment technologies and price-induced energy saving, and can illustrate optimal energy supply structures by minimizing the costs of total discounted energy systems at a discount rate of 5%. The following parameters of nuclear fusion were considered: cost of electricity (COE) in the nuclear fusion introduction year, annual COE reduction rates, regional introduction year, and regional nuclear fusion capacity projection. The investigations are carried out for three nuclear fusion projections one of which includes tritium breeding constraints, four future CO2 concentration constraints, and technological assumptions on fossil fuels, nuclear fission, CO2 sequestration, and anonymous innovative technologies. It is concluded that: (1) the BPs are from 65 to 125 mill kW−1 h−1 depending on the introduction year of nuclear fusion under the 550 ppmv CO2 concentration constraints; those of a business-as-usual (BAU) case are from 51 to 68 mill kW−1h−1. Uncertainties resulting from the CO2 concentration constraints and the technological options influenced the BPs by plus/minus some 10–30 mill kW−1h−1, (2) tokamak-type nuclear fusion reactors (as presently designed, with a COE range around 70–130 mill kW−1h−1) would be favourably introduced into energy systems after 2060 based on the economic criteria under the 450 and 550 ppmv CO2 concentration constraint, but not selected under the BAU case and 650 ppmv CO2 concentration constraint, and (3) the share of electricity in 2100 produced by the presently designed tokamak-type nuclear fusion reactors (introduced after 2060) is well below 30%. It should be noted that these conclusions are based upon varieties of uncertainties in scenarios and data assumptions on nuclear fusion as well as technological options.

Learning curves and technology assessment

This paper uses the formal concept of learning curves to analyse regular behaviour of performance improvements in various energy technologies. The concept allows the estimation of a single indicator of technological progress, the learning rate, which expresses the constant percentage improvement (usually in terms of cost reductions) in a technology for each doubling of the technologies cumulative installed capacity. We present 42 energy-related learning rates, either calculated directly from available data or assembled from the literature. We elaborate briefly on eight of these to illustrate issues addressed by technology assessments to convert these raw historical learning rates into prospective learning rate distributions for use in long-term energy models. The paper includes a sensitivity analysis of policy- relevant variables with respect to learning rates, a discussion of possible extensions and limitations of the approach and an outlook on future work in the field.

Learning rates for energy technologies

Technological learning, i.e., cost reductions as technology manufacturers accumulate experience, is increasingly being incorporated in models to assess long-term energy strategies and related greenhouse gas emissions. Most of these applications use learning rates based on studies of non-energy technologies, or sparse results from a few energy studies. This report is a step towards a larger empirical basis for choosing learning rates (or learning rate distributions) of energy conversion technologies for energy models. We assemble data on experience accumulation and cost reductions for a number of energy technologies, estimate learning rates for the resulting 26 data sets, analyze their variability, and evaluate their usefulness for applications in long-term energy models.

Global Energy -- Assessing the Future

The energy problem is one of cost; i.e., the financial costs of developing resources and the real costs of maintaining an acceptable environmental quality. In forecasting the future of global energy production and use, the authors examine alternative means of producing and transforming energy (including nuclear and solar power, shale oil, biomass, coal, and synthetic fuels), and the key factors influencing energy consumption, such as population and gross national product. Using the IEA/ORAU Long-Term Global Energy Model, they develop a baseline forecast of global energy production and use, but explore alternate futures. They conclude that the long-term global energy system will slowly evolve into a diversity of options that will grow rather than shrink. They also conclude that we must learn to live and plan with uncertainty about the details of the future. 60 figures, 120 tables.