The Implications for Renewable Energy Innovation of Doubling the Share of Renewables in the Global Energy Mix between 2010 and 2030

Saygin, Deǧer; Kempener, Ruud; Wagner, Nicholas; Ayuso, Marı́a Jesús; Gielen, Dolf

doi:10.3390/en8065828

Cited by 69 publications

(22 citation statements)

References 27 publications

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“…REmap is based on a bottom-up analysis of existing renewable energy plans and additional renewable energy options between 2010 and 2030 in 26 countries located on five different continents (Saygin et al 2015). For each country analysis, IRENA works together with a REmap expert nominated by the country.…”

Section: Remap Methodologymentioning

confidence: 99%

Informing Energy and Climate Policies Using Energy Systems Models

Giannakidis¹,

Labriet²,

Gallachóir³

et al. 2015

Lecture Notes in Energy

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study of energy: from science and engineering to the analysis of energy policy. The series' scope includes but is not limited to, renewable and green energy, nuclear, fossil fuels and carbon capture, energy systems, energy storage and harvesting, batteries and fuel cells, power systems, energy efficiency, energy in buildings, energy policy, as well as energy-related topics in economics, management and transportation. Books published in LNE are original and timely and bridge between advanced textbooks and the forefront of research. Readers of LNE include postgraduate students and non-specialist researchers wishing to gain an accessible introduction to a field of research as well as professionals and researchers with a need for an up-to-date reference book on a well-defined topic. The series publishes single and multi-authored volumes as well as advanced textbooks.More information about this series at

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Section: Remap Methodologymentioning

confidence: 99%

Informing Energy and Climate Policies Using Energy Systems Models

Giannakidis¹,

Labriet²,

Gallachóir³

et al. 2015

Lecture Notes in Energy

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show abstract

“…The modelling approach involves a techno-economic assessment of the energy system developments for the energy transformation (power and district heat) and end-use (residential, commercial and public buildings, manufacturing industry and transport) sectors. Kempener et al (2015) and Saygin et al (2015) provide further details of the REmap methodology, discuss its strengths and limitations and compare its findings with other widely used global energy scenario models.…”

Section: Estimation Of the Electricity Generation Capacity MIX Using mentioning

confidence: 99%

Power sector asset stranding effects of climate policies

Saygin¹,

Rigter²,

Caldecott

et al. 2019

Energy Sources, Part B: Economics, Planning, and Policy

Self Cite

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Energy sector decarbonization to limit the temperature rise to well-below 2 degrees Celsius will result in stranded assets and capital stock replacement before its technical lifetime ends. In this paper, stranded assets in the global power sector are quantified based on a simplified bottom-up analysis that considers the capital stock turnover of fossil fuel-fired power plants in the G20 countries between 2015 and 2050. Power sector transformation starting now based on accelerated deployment of renewables results in US dollar (USD) 927 billion of global power sector stranded assets by 2050. Stranded coal assets would represent around three-quarters of total stranded assets value and China alone would represent 45% of the total. Delaying action to mitigate climate change until 2030 doubles stranded asset value. Countries should consider assets' age profile characteristics in their decision making. Early action and avoidance of investments in new carbon-intensive assets can minimize stranded asset risks.

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“…The relative accuracy [29][30][31] is set by the researcher, and this section uses 0.15, 0.2, 0.25, and 0.3, respectively. The optimum sample capacity estimation under different allowable error accuracy values is shown in Figure 5.…”

Section: Based On the Optimal Sample Capacity Estimate To Determine Tmentioning

confidence: 99%

Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

Wang

Zheng

2017

Energies

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Time interval and time length are two important indexes when analyzing the active output data of photovoltaic (PV) power stations. When the time interval is constant, the length of time is too small, and the included information is less, resulting in a lack and distortion of information; it the length of time is too large, the included information is redundant and complicated, resulting in unnecessary increases of storage capacity and calculation. Therefore, it is important to determine the appropriate length of data for the analysis of PV output data. In this paper, firstly, the output data of a PV power station is analyzed statistically, and the preliminary conclusions for time length selection are obtained by autocorrelation analysis. Based on the weather characteristics, clustering analysis methods and statistical principles are used to analyze the data and optimal sample capacity estimation, respectively, for different types of photovoltaic output data and determine the required data time length at the time of analyzing the PV power plant output data, the relationship between energy storage capacity demand and data length is investigated, the rationality of the length of the selected time is verified. Meanwhile, the energy storage system capacity configuration based on the optimal data time length is given. The results show that the requirement of data volume of energy storage system capacity configuration can be met when the time length of the PV output data is 23 days.

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The Implications for Renewable Energy Innovation of Doubling the Share of Renewables in the Global Energy Mix between 2010 and 2030

Cited by 69 publications

References 27 publications

Informing Energy and Climate Policies Using Energy Systems Models

Informing Energy and Climate Policies Using Energy Systems Models

Power sector asset stranding effects of climate policies

Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

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