Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data

Korfiati, Athina; Gkonos, Charalampos; Veronesi, Fabio; Gaki, Ariadni; Grassi, Stefano; Schenkel, Roland; Volkwein, Stephan; Raubal, Martin; Hurni, Lorenz

doi:10.5278/ijsepm.2016.9.3

Cited by 9 publications

(11 citation statements)

References 13 publications

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“…We developed a PV energy yield estimation model by modifying the global solar atlas (GSA) algorithm (ESMAP2.0 2019), which allows exploration of PV potential responses to a range of environmental and technological factors (Kawajiri et al 2011, Green et al 2021, ESMAP2.0 2019; see also details in the next section and table 1. Modifications were made to include the spatial influence of air temperature and orientation: PV efficiency has been found to decline by around 0.4% for every 1 • C increase in temperature above 25 • C, while the direction and tilt angle of a PV panel determines the amount of absorbed incident radiation and varies with latitude (Kawajiri et al 2011, Korfiati et al 2016. Wind could play an important role (positive or negative, depending on the balance of its effects on temperature and soiling), but wind speed data have very large uncertainties that make reliable projections impossible at this scale, and so these are not included.…”

Section: Methodsmentioning

confidence: 99%

Modelling the global photovoltaic potential on land and its sensitivity to climate change

Saxena,

Brown,

Arneth

et al. 2023

Environ. Res. Lett.

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Solar photovoltaic (PV) energy is fundamental for decarbonizing the global economy and supporting the renewable energy transitions that are needed to combat climate change. Potential solar power production at a given location is a function of climatic variables that will change over time and so climate change needs to be accounted for in PV potential estimation. The future potential of PV in response to climate change has not previously been assessed consistently and globally across alternative scenarios. We develop global gridded estimates of PV potential between 2020 and 2100 as a function of spatial, climatic, technological and infrastructural conditions. We find a global technical potential of 175 111 T W h yr−1 in 2050, which changes by between ca. −19% (high-emission scenario) and +16% (low-emission scenario), with larger geographic variations within these scenarios. We perform a sensitivity analysis to identify key uncertainties and assess the scope for emerging PV technologies to offset negative climate impacts. We find that suboptimal orientation and temperature losses have the largest negative effects (reducing PV potential by up to ca. 50% and ca. 10% respectively), but that new technologies may be able to generate gains of more than 200% if successfully deployed worldwide. Solar power can make an important contribution to energy production over the coming decades and the demand for renewable energy could be met by PV deployment on between 0.5% and 1% of the global land area, provided its deployment accounts for the location-specific impacts of climate change.

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Section: Methodsmentioning

confidence: 99%

Modelling the global photovoltaic potential on land and its sensitivity to climate change

Saxena,

Brown,

Arneth

et al. 2023

Environ. Res. Lett.

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“…Worldwide, the technical potential of solar PV systems is assessed most widely and most comprehensively [25,28,[30][31][32][35][36][37][38]40,43,45,47], followed by the technical and economic potential [33,34,46], the technical and market potential [41], the theoretical potential [49], the economic potential [29], and the theoretical, practical and economic potential [42]. Historically, Lithuanian scientists [50,51] have paid attention to the technical and market potential of solar PV systems in the country.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The potential of different solar PV systems was assessed by employing different methodologies, including top-down [37] and bottom-up [49] approaches, high-resolution satellite image analysis techniques [35], spatial analysis [28], high-resolution geospatial energy supply modeling [32], geospatial analysis modeling [29,44,45], 3D city models combined with modeling functions [34], the NREL System Advisor Model [38], Dynamic Energy Atlas software [31], statistical analysis [36], the GIS method [30,36,43], machine learning [33,45], statistical methods [47] and LCOE [49]. In [48], a methodology combining machine learning algorithms with GIS and physical models was applied.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The applied methods were provided with satellite and statistical data [33], satellite imagery and weather data [45], temperature data [37], light detection and ranging (LiDAR) data [36,39], geographic information [38], data on the availability and quality of renewable resources, technical system performance [28], differences in local resources, the cost and revenue potential of technology-specific sites [29], the long-term distribution of solar resources (global horizontal irradiation and direct normal irradiation), the configuration of the PV system, the conversion efficiency of PV panels, the shading and soiling of the panels [42], high-spatial-temporal resolution climate data, the most common PV modules, the technical, policy and economic constrains [43], data on solar resource availability, panel efficiency, spacing factors and available area [30], cadastral and land use data [46] and economic data [49]. Constraints were added related to policy frameworks and regulations at the level of regions in the country [31], topography, environmental concern and landuse [28].…”

Section: Literature Reviewmentioning

confidence: 99%

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Assessment of Technical and Economic Potential of Urban Rooftop Solar Photovoltaic Systems in Lithuania

et al. 2023

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This paper aimed at assessing the technical and economic potential of using rooftop solar photovoltaic (PV) systems in Lithuanian urban areas to support energy and climate policy formation and its implementation in the country. A bottom-up approach was applied. A number of apartment (AP), commercial (COM) and public (PUB) buildings, electric vehicle (EV) charging infrastructures and solar PV systems in the largest Lithuanian cities were analyzed, and the results were tailored to national-wide estimates. LiDAR and photogrammetry data of building topologies, geographical information systems (GIS), digital maps for rooftop measurements, factual solar photovoltaic (PV) performance modeling, average profiles of solar PV systems, statistical data of energy consumption in buildings, levelized cost of energy (LCOE) models and the literature review method were used. The results of the assessment of selected buildings and their categories reveal that the rooftop area per installed PV unit was 14.1–18.3 m2/kW in AP buildings, followed by 18.0–18.6 m2/kW in COM and 6.1–18.3 m2/kW in PUB buildings; the factor of rooftop suitability for solar PV system installation varied in ranges of 31.5–85.4%, 48.9–99.4% and 50.1–88.2%, respectively. Corrected by expert judgement, these estimates served as inputs for the assessment of technical and economic potentials. Furthermore, energy generated by solar PV could be consumed locally with little supplied to the distribution grid, resulting in no or a limited need for expensive investments in infrastructure. In addition, locating an EV charger on the premises of COM or PUB buildings could improve the localized consumption of solar energy. Thus, rooftop solar PV systems in urban Lithuanian areas are preferable. The results of the technical potential analysis show that, on the rooftops of AP buildings, 272 MW could be installed, followed by 78 MW on roofs of PUB and 67 MW on COM buildings. If technical potential is extracted, EUR 332.7 million can be invested. The cost of energy produced by solar PV systems was estimated at 2.34–5.25 EURct/kWh, which is significantly lower than the prices of market and retail electricity, standing at 23 EURct/kWh and 24 EURct/kWh (with support from the government) in 2022, respectively. The research’s limitations are defined by the fact that average values were applied to estimate the technical and economic potential of rooftop solar PV systems; a limited number of building categories were analyzed; the category of private houses, which is minor in cities, was missed, and rural areas with a great number of private houses were not covered by this study.

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“…In addition, several analyses have investigated photo voltaics. This includes analyses from a spatial perspective [28][29][30] and analyses focusing on acceptance and adaption [5,[31][32][33].…”

Section: Poul Alberg øStergaard* Rasmus Magni Johannsenmentioning

confidence: 99%

Sustainable Energy Planning and Management Vol 37

Østergaard

Johannsen

2023

IJSEPM

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This 37th volume of the International Journal of Sustainable Energy Planning and Management present novel analyses on the Nigerian electricity sector with focus on stakeholders in centralised and decentralised electricity supply. Analyses of Berlin-Brandenburg in Germany shows the prospects of 100 % renewable energy systems here. Industry is an important target in the energy transition, so a model is developed to analyse energy savings potentials. Within the heating sector, much of the individual dwellings in Central and Eastern Europe are impacted by a fuel trap, trapping them between two essentially undesirable options – biomass and natural gas. Also, within the residential sector, a new analysis probes in the feasibility of installing photo voltaics in India, and lastly, an interesting article investigates the local economic and employment effects of increased biofuel production.

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Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data

Cited by 9 publications

References 13 publications

Modelling the global photovoltaic potential on land and its sensitivity to climate change

Modelling the global photovoltaic potential on land and its sensitivity to climate change

Assessment of Technical and Economic Potential of Urban Rooftop Solar Photovoltaic Systems in Lithuania

Sustainable Energy Planning and Management Vol 37

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