Induced Technological Change in a Limited Foresight Optimization Model

The threat of global warming calls for a major transformation of the energy system the coming century. Modeling technological change is an important factor in energy systems modeling. Technological change may be treated as induced by climate policy or as exogenous. We investigate the importance of induced technological change (ITC) in GET-LFL, an iterative optimization model with limited foresight that includes learning-by-doing. Scenarios for stabilization of atmospheric CO2 concentrations at 400, 450, 500 and 550 ppm are studied. We find that the introduction of ITC reduces the total net present value of the abatement cost over this century by 3-9% compared to a case where technological learning is exogenous. Technology specific polices which force the introduction of fuel cell cars and solar PV in combination with ITC reduce the costs further by 4-7% and lead to significantly different technological solutions in different sectors, primarily in the transport sector.Energy system model, Limited foresight, Climate policy, Endougenous learning, Technological lock-in

The error induced by using representative periods in capacity expansion models: system cost, total capacity mix and regional capacity mix

Capacity Expansion Models (CEMs) are optimization models used for long-term energy planning on national to continental scale. They are typically computationally demanding, thus in need of simplification, where one such simplification is to reduce the temporal representation. This paper investigates how using representative periods to reduce the temporal representation in CEMs distorts results compared to a benchmark model of a full chronological year. The test model is a generic CEM applied to Europe. We test the performance of reduced models at penetration levels of wind and solar of 90%. Three measures for accuracy are used: (i) system cost, (ii) total capacity mix and (iii) regional capacity. We find that: (i) the system cost is well represented (similar to 5% deviation from benchmark) with as few as ten representative days, (ii) the capacity mix is in general fairly well (similar to 20% deviation) represented with 50 or more representative days, and (iii) the regional capacity mix displays large deviations (> 50%) from benchmark for as many as 250 representative days. We conclude that modelers should be aware of the error margins when presenting results on these three aspects

Assessing biorefineries

Biomass, a product of the solar energy influx and the synthesis of carbon dioxide and water, has been used since the dawn of humanity, always as a source of food and as a source of energy and materials since the invention of controlled fire and simple tools some hundred thousand years ago. The transition from hunting and gathering to agriculture has over the last five millennia led to a rapid increase of world population and a human dominance over the Earth’s land surface and biota

The cost of a future low-carbon electricity system without nuclear power – the case of Sweden

To achieve the goal of deep decarbonization of the electricity system, more and more variable renewable energy (VRE) is being adopted. However, there is no consensus among researchers on whether the goal can be accomplished without large cost escalation if nuclear power is excluded in the future electricity system. In Sweden, where nuclear power generated 41% of the annual electricity supply in 2014, the official goal is 100% renewable electricity production by 2040. Therefore, we investigate the cost of a future low-carbon electricity system without nuclear power for Sweden. We model the European electricity system with a focus on Sweden and run a techno-economic cost optimization model for capacity investment and dispatch of generation, transmission, storage and demand-response, under a CO2 emission constraint of 10 g/kWh. Our results show that there are no, or only minor, cost benefits to reinvest in nuclear power plants in Sweden once the old ones are decommissioned. This holds for a large range of assumptions on technology costs and possibilities for investment in additional transmission capacity. We contrast our results with the recent study that claims severe cost penalties for not allowing nuclear power in Sweden and discuss the implications of methodology choice

Scenarier för klimatpåverkan från matkonsumtionen 2050

Denna rapport är framtagen inom ramen för Mistra Urban Futures-projektet WISE - Well-being In Sustainable cities, och mer specifikt inom delprojektet Klimatomställning Göteborg: potentialer och livskvalitetseffekter. Göteborgarnas klimatpåverkan idag och potentialer för framtiden beräknas avseende boende, transporter, mat, etc. Detta kan ge en samlad bild av möjligheterna att nå klimatmålen. En livskvalitetskoppling är att denna överblick kan ge underlag för en samhällsdiskussion om vilka vägar för att uppnå klimatmålen som människor föredrar. Syftet med denna underlagsrapport är att beräkna potentialer för utsläppsminskningar till 2050 från svenskarnas matkonsumtion. Beräkningar har gjorts för år 2006, samt för nio alternativa scenarier för 2050

An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand

Historical wind deployment and implications for energy system models

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation

Trends in greenhouse gas emissions from consumption and production of animal food products - implications for long-term climate targets

To analyse trends in greenhouse (GHG) emissions from production and consumption of animal products in Sweden, life-cycle emissions were calculated for the average production of pork, chicken meat, beef, dairy and eggs in 1990 and 2005. The calculated average emissions were used together with food consumption statistics and literature data on imported products to estimate trends in per capita emissions from animal food consumption. Total life cycle emissions from the Swedish livestock production were around 8.5 Mt carbon dioxide equivalents (CO2e) in 1990 and emissions decreased to 7.3 Mt CO2e in 2005 (14% reduction). Around two-thirds of the emission cut was explained by more efficient production (less GHG emission per product unit) and one third was due to a reduced animal production. The average GHG emissions per product unit until the farm-gate were reduced by 20% for dairy, 15% for pork and 23% for chicken meat, unchanged for eggs and increased by 10% for beef. A larger share of the average beef was produced from suckler cows in cow-calf systems in 2005 due to the decreasing dairy cow herd, which explains the increased emissions for the average beef in 2005. The overall emissions cuts from the livestock sector were a result of several measures taken in farm production, for example increased dairy yield per cow, lowered use of synthetic nitrogen fertilisers in grasslands, reduced losses of ammonia from manure and a switch to biofuels for heating in chicken houses. In contrast to production, total GHG emissions from the Swedish consumption of animal products increased by around 22% between 1990 and 2005. This was explained by strong growth in meat consumption based mainly on imports, where growth in beef consumption especially was responsible for most emission increase over the 15-year period. Swedish GHG emissions caused by consumption of animal products reached around 1.1 tonnes CO2e per capita in 2005. The emission cuts necessary for meeting a global temperature-increase target of 2 degrees might imply a severe constraint on the long-term global consumption of animal food. Due to the relatively limited potential for reducing food-related emissions by higher productivity and technological means, structural changes in food consumption towards less emission intensive food might be required for meeting the 2-degree target