Yunshu Li’s research while affiliated with International Energy Agency (IEA) and other places

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Publications (2)


The MSR toolset comprises of five Python scripts that are run sequentially. A high-level description of each script and process flow is illustrated in this figure.
Process of MSR creation. This schematic shows the various steps of MSR creation (see also Methods), starting from (i) the boundaries of a hypothetical rectangular country, through (ii) the exclusion of unsuitable areas, (iii) the classification of the suitable areas into different bins representing VRE resources of different strength, (iv) the polygonization of the areas in each bin, and (v) the breakdown of each polygon into smaller cells, to arrive at (vi) a collection of pre-screened MSRs, each with their own specific characteristics, for the country.
Spatial distribution of solar PV and wind MSRs across Africa. (a) A map of the African continent showing all solar PV and wind MSRs screened by LCOE up to a maximum coverage of 5% of a country’s area. (b,c) Example temporal profiles (diurnal and seasonal) for the two example locations indicated in (a). The diurnal example in (b) covers the 12th day of March.
Capacity factor of MSRs as compared to their distance from the transmission grid. The left axis shows the average distance from the transmission grid across all MSRs in a country; the right axis shows the average capacity factor. Averages are weighted by MSR area. Solar PV and wind MSRs differ in (a) the spatial divergence of CFs (larger for wind than solar PV) and in (b) the distance-from-grid of the cheapest MSRs (higher for wind than solar PV). Country abbreviations denote alpha-2 codes; see Supplementary Table 4 for the list of full names. Countries are ranked vertically according to the alphabetical order of these full names. Note that some countries do not have any viable wind power potential according to the present methodology, hence bars for wind power are omitted for those countries in this graph.
MSRs classified by expected LCOE, including installation costs, operation and maintenance costs, transmission grid extension costs and road network extension costs. (a) All-Africa MSRs, screened by LCOE up to coverage of 5% of a country’s area, classified by five LCOE categories from cheaper to costlier. (b) The country-level area-weighted distribution of MSRs across these five categories. Country abbreviations denote alpha-2 codes; see Supplementary Table 4 for a list of full names. Countries with comparatively low overall VRE potential are marked with symbols (*), (**) or (***) if total MSR area covered less than 3%, 1% and 0.1% of the country, respectively, and with (−) in case of absence of MSRs in that country. (c,d) Each country’s average LCOE as function of average CF (averages weighted by MSR area), for solar PV (c) and wind (d). SO = Somalia, NA = Namibia, UG = Uganda, DJ = Djibouti, TD = Chad, TN = Tunisia, CM = Cameroon.
An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power
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October 2022

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Bilal Hussain

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Daniel Russo

With solar and wind power generation reaching unprecedented growth rates globally, much research effort has recently gone into a comprehensive mapping of the worldwide potential of these variable renewable electricity (VRE) sources. From a perspective of energy systems analysis, the locations with the strongest resources may not necessarily be the best candidates for investment in new power plants, since the distance from existing grid and road infrastructures and the temporal variability of power generation also matter. To inform energy planning and policymaking, cost-optimisation models for energy systems must be fed with adequate data on potential sites for VRE plants, including costs reflective of resource strength, grid expansion needs and full hourly generation profiles. Such data, tailored to energy system models, has been lacking up to now. In this study, we present a new open-source and open-access all-Africa dataset of “supply regions” for solar photovoltaic and onshore wind power to feed energy models and inform capacity expansion planning.

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Capacity and generation mix in the Reference and National Target scenarios
The Renewable Energy Transition in Africa

Complementing the work on "The Renewable Energy Transition in Africa", IRENA explored the transformational potential in 5 African countries, highlighting the new paradigm of the power supply chains in Côte d’Ivoire, Ghana, Morocco, Rwanda and South Africa. It shows real pilot projects and demonstrates how countries can a) take advantage of the abundancy and competitiveness of renewables, b) align ambitious renewable targets in energy and climate plans, c) continue supporting the development of regional markets, d) leverage renewables and distributed energy resources to achieve universal energy access, e) develop tailored power sector transformation plans based on a systemic innovation approach, and f) build on policy frameworks for just and inclusive transition.

Citations (1)


... Onshore wind turbines were found to be positively associated with wind power density, air density, and wind speed [10,11,13]. In the context of solar PV installations, an ample influx of solar radiation fuels energy generation [9,14,15]. Yet, more subtle factors such as humidity and air temperature can negatively affect the energy potential, possibly reducing the likelihood of installation occurrence [16,17]. Elevation remains subject to scholarly debate regarding its influence on solar PV placement. ...

Reference:

Location determinants of industrial solar photovoltaics and onshore wind turbines in the EU
An all-Africa dataset of energy model “supply regions” for solar photovoltaic and wind power

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