How to characterize flexibility requirements of highly renewable energy systems over multiple timescales

This thesis addresses the question: Can we characterize requirements for flexibility over different timescales in electricity systems dominated by solar and wind power? Storage and flexibility play an increasingly important role, but there is great uncertainty about amounts of flexibility needed and how they depend on generation mix and demand, including electrification of heating and transportation. Furthermore, to inform investment and planning, it is valuable to characterize the timescales over which flexibility will be needed, as different resources can be used to shift energy over different time horizons. To address this problem, the analysis uses the novel application of three methods to disaggregate overall flexibility requirements into short-, medium-, and long-term requirements, without relying on assumptions about technology parameters or costs. These methods are illustrated using the case of Great Britain and results are used to draw insights into GB flexibility needs under future scenarios. Flexibility is required over multiple timescales, from less than hourly to interseasonal or longer. Overcapacity of renewables provides value in terms of avoided storage costs, particularly displacing requirements for the longest duration storage, though generation capacity beyond 120% of demand yields diminishing marginal returns. Heating electrification has a larger impact than EVs, though flexible heating can partially offset additional power capacity needs. In all cases, the capacity required to shift energy by up to a day was on the order of 1 TWh; this could account for over half of all energy shifted depending on flexible resource operation. Electricity systems with at least 80% of energy from solar and wind require 3-150 TWh to shift energy by weeks or longer. The required capacity to shift energy by more than one year could potentially be avoided using renewables overcapacity, dispatchable generation, or interconnectors

Modeling environmental impact of unfired bricks in India

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Materials Science and Engineering, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 30-32).Brick manufacturing requires a considerable amount of energy and land, but these numbers have been difficult to quantify in rural parts of the developing world. The environmental impact of unfired bricks in India is investigated through modeling the effects of materials composition and processing on energy consumption, carbon dioxide equivalent emissions, and land surface area use. The analysis uses a cradle-to-gate life cycle assessment to quantitatively estimate these impacts. The depth of soil extraction has a significantly affects the land use required for bricks; changing this depth in practice or through regulation has the potential to reduce environmental impact without affecting brick performance. The impact of unfired bricks depends greatly on composition, in particular the amount and type of stabilizer and the incorporation of fly ash. While stabilizers increase the environmental burden, the performance gain is potentially worth these effects when compared to energy intensive fired bricks. Future work could expand the model to quantify the relevant cost and performance tradeoffs with environmental impact.by Miriam E. Zachau Walker.S.B

Change in cooling degree days with global mean temperature rise increasing from 1.5 °C to 2.0 °C

Limiting global mean temperature rise to 1.5 °C is increasingly out of reach. Here we show the impact on global cooling demand in moving from 1.5 °C to 2.0 °C of global warming. African countries have the highest increase in cooling requirements. Switzerland, the United Kingdom and Norway (traditionally unprepared for heat) will sufer the largest relative cooling demand surges. Immediate and unprecedented adaptation interventions are required worldwide to be prepared for a hotter world

Ensemble of global climate simulations for temperature in historical, 1.5 °C and 2.0 °C scenarios from HadAM4

Large ensembles of global temperature are provided for three climate scenarios: historical (2006–16), 1.5 °C and 2.0 °C above pre-industrial levels. Each scenario has 700 members (70 simulations per year for ten years) of 6-hourly mean temperatures at a resolution of 0.833° ´ 0.556° (longitude ´ latitude) over the land surface. The data was generated using the climateprediction.net (CPDN) climate simulation environment, to run HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Biases in simulated temperature were identified and corrected using quantile mapping with reference temperature data from ERA5. The data is stored within the UK Natural and Environmental Research Council Centre for Environmental Data Analysis repository as NetCDF V4 files

Changes in Cooling Degree Days (CDD) between the 1.5ºC and 2.0ºC global warming scenarios: Data to replicate global maps on absolute and relative changes in cooling degree days (from a 1.5ºC to a 2.0ºC global warming scenario)

These NetCDF V4 files (*.nc) contain the absolute and relative mean increase of cooling degree days (CDDs) from 1.5ºC to 2ºC global warming scenarios. Additionally, the standard deviation is provided. The data has a horizontal resolution of 0.833 longitude and 0.556 latitude over the land surface. These annual CDDs and standard deviation globally were calculated using an ensemble of 700 simulations per climate change scenario. Cooling degree days (CDDS) were calculated for the ensemble members using the temperature threshold of 18ºC. Then, annual mean CDDs and standard deviation per coordinate across ensemble members were obtained for the 1.5ºC and 2ºC scenarios. Finally, absolute and relative differences between 1.5ºC and 2ºC were computed. The climate data, involving 700 simulations per scenario, was generated using the HadAM4P Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre. Three scenarios were generated: historical (2006-16), 1.5ºC and 2ºC. The simulation outputs were mean temperatures with a 6-hour timestep and a horizontal resolution of 0.833 longitude and 0.556 latitude. Simulations took place within climateprediction.net (CPDN) climate simulation, which uses the Berkeley Open Infrastructure for Network Computing (BOINC) framework. Biases in simulated temperature were identified and corrected using a quantile mapping approach

Designing a zero-order energy transition model: How to create a new Starter Data Kit

The Paris Agreement was signed by 192 Parties, who committed to reducing emissions. Reaching such commitments by developing national decarbonisation strategies requires significant analyses and investment. Analyses for such strategies are often delayed due to a lack of accurate and up-to-date data for creating energy transition models. The Starter Data Kits address this issue by providing open-source, zero-level country datasets to accelerate the energy planning process. There is a strong demand for replicating the process of creating Starter Data Kits because they are currently only available for 69 countries in Africa, Asia, and South America. Using an African country as an example, this paper presents the methodology to create a Starter Data Kit made of tool-agnostic data repositories and OSeMOSYS-specific data files. The paper illustrates the steps involved, provides additional information for conducting similar work in Asia and South America, and highlights the limitations of the current version of the Starter Data Kits. Future development is proposed to expand the datasets, including new and more accurate data and new energy sectors. Therefore, this document provides instructions on the steps and materials required to develop a Starter Data Kit. • The methodology presented here is intended to encourage practitioners to apply it to new countries and expand the current Starter Data Kits library. • It is a novel process that creates data pipelines that feed into a single Data Collection and Manipulation Tool (DaCoMaTool). • It allows for tool-agnostic data creation in a consistent format ready for a modelling analysis using one of the available tools

CCG Starter Data Kit: Papua New Guinea

A starter data kit for Papua New Guine

CCG Starter Data Kit: Bolivia

A starter data kit for Bolivi

CCG Starter Data Kit: Uruguay

A starter data kit for Urugua