Solar resource and PV potential of Zambia: Solar Resource Atlas. Washington, DC:
accurate solar resource and meteorological data, used in solar energy yield
With its year-round sunshine and geographical location, Zambia is well
Over the past decade, the cost of renewable energy has been declining steadily. The weighted average of the levelized cost of electricity at the global level fell by 15% for wind and 13% for solar energy in 2021 alone (Irena 2022). Over the past 5 years, the cost of solar energy has been declining at the rate of 13% per year while it was 7% for onshore wind energy (Lazard 2020). These trends have the potential to incentivize carbon neutrality and contribute to tackling the climate crisis by reducing reliance on fossil fuels (Pfeifer et al. 2021; Luderer et al. 2021).
While critiques of renewable energy cite the spatiotemporal variability of wind and solar radiation as a weakness to a dependable electricity grid (Diesendorf and Elliston 2018), this shortcoming can be overcome by renewable energy mixes including wind, water, and solar power. Furthermore, technological advancements such as innovative energy storage, and optimized management techniques show promise of turning wind and solar energy resources into dependable electricity grids. Indeed, renewable energy sources like solar and wind energy are constantly being replenished at a higher rate than what humans can consume.
Many countries in the developed and developing world are turning to renewable energy as a pathway to climate change mitigation. The European Union for instance is accelerating the take-up of renewables to significantly contribute to the reduction of net greenhouse gas emissions by at least ~ 55% before the end of 2030 (EU 2022). In Africa, many countries including but not limited to South Africa, Nigeria, and Kenya have also set strong emissions-reduction targets. Zambia has also committed to reducing emissions by 25% by 2030 (USAID 2015). Overall, Africa has committed to cutting 32% of emissions by 2030 (Abudu et al. 2023).
The overarching objective of this study is to explore future variations of climatic variables that are relevant to future photovoltaic solar power resources (PVRes) in Zambia. While much of Zambia experience roughly similar climate characteristics due to the plateau that characterizes the country''s topography (Fig. 1A), a few climatic differences exist, and these can be classified into four main categories of the Köppen–Geiger classification (Peel et al. 2007):
Tropical Savanna The Tropical Savanna climate which is classified as Aw in the Köppen–Geiger classification covers Kalabo district, parts of Shang''ombo, and Mongu in the Western Province of Zambia (Fig. 1B). In the Eastern Province, Katete, Petauke, and parts of Chipata are also classified as Tropical Savana. These areas generally experience a pronounced dry season characterized by monthly rainfall averaging 60 mm (Africa Groundwater Atlas 2019).
Arid Steppe This climate zone covers the semi-arid region of Livingstone, Kaloma, Choma, and parts of the Luangwa valley (Fig. 1B). While the rainfall in these areas is not as low as that of desert climates, it is usually less than potential evapotranspiration and can, thus, be described as semi-arid.
Temperate with dry winters (generally June–August, see Marshall 2017) and warm summers The Cwb climate of Zambia mainly covers the northern tip of the country bordering the Democratic Republic of the Congo and Tanzania (Fig. 1B). These areas include Kaputa, Mpulungu, and Mbala. Given their high elevations, temperatures are usually lower than across the rest of the country.
Temperate with dry winters and hot summers Classified as Cwa in the Köppen–Geiger classification (Peel et al. 2007), this climate zone covers the rest of the country which is mainly characterized by dry winters and wet summers.
Overview of the study area showing: A the location of Zambia in Southern Africa (green square). The grey shading indicates topographical variations across the region based on the Global Land 1 km Base Elevation (GLOBE) digital elevation model (Hastings and Dunbar, 1999). The blue shading shows the location of major water bodies, B climatic zones of Zambia were developed using the Climatic Research Unit Time Series Version 3.21 (CRU TS 3.21) dataset produced and maintained by the Climatic Research Unit of the University of East Anglia (Jones and Harries 2013). The precipitation and temperature CRU data used to produce the Climatic Zones of Zambia are for the period 1951–2010 (Africa Groundwater Atlas 2019)
To perform the present analyses, we explored three different atmospheric variables which include air temperature measured at 2 m above sea level (TAS), wind speed measured at 10 m above sea level (WS), and downwelling surface shortwave solar radiation (SR).
TAS was sourced from the latest version of high-resolution monthly data (CRU TS v4.05) from the Climatic Research Unit of the University of East Anglia (Harris et al. 2020; Table 1). The dataset covers all land areas across the globe apart from Antarctica and is gridded at a resolution of 0.5°. CRU TS v4.05 was developed using angular-distance weighting to interpolate in situ data from a dense network of meteorological stations across the globe (Harris et al. 2020).
For wind speed, we used the fifth-generation global climate reanalysis (ERA5). ERA5 is a product of the European Centre for Medium-Range Weather Forecasts (ECMWF) and was developed by assimilating in situ data into a global weather forecast model (Hersbach et al. 2020). ERA5 has a 0.1° × 0.1° horizontal resolution.
We retrieved SR from the archives of TerraClimate. This is also a high-resolution dataset gridded at 4 km (Abatzoglou et al. 2018). TerraClimate was developed by merging climatological normals from WorldClim version 1.4 and version 2 datasets (Fick and Hijmans 2017), CRU TS v4.0 (Harris et al. 2020), and JRA-55 (Ebita et al. 2011).
In this study, we analyzed CORDEX-CORE models. Unlike ordinary CORDEX models, the CORDEX-CORE initiative is an improvement in terms of the horizontal resolution and a general homogenization of simulations across different CORDEX domains (Gutowski et al. 2016). CORDEX-CORE Regional Climate Models (RCMs) can, therefore, be thought of as a homogeneous set of high-resolution projections across all CORDEX domains driven by a common set of General Circulation Models (GCMs).
It is important to note that CORDEX-CORE RCMs are only available for two Representative Concentration Pathways (RCPs), i.e., the low-end RCP2.6 and the high-end RCP8.5 scenario (Giorgi et al. 2022). RCP 2.6 is an ambitious target of keeping global temperature increments below 2 °C by the close of the twenty-first century (IPCC 2014). To achieve the RCP2.6 target, emissions should decline and reach zero by 2100. On the other hand, the RCP8.5 is a business-as-usual scenario; emissions continue to rise and as such, it is considered a worst-case scenario (Meinshausen et al. 2011). In this study, we examined both scenarios over the period 2025–2100 (Table 2). We retrieved all simulations from the Earth System Grid Federation (ESGF) node of the German Climate Computing Centre (DKRZ).
To establish a reliable understanding of future PVRes, the RCMs used must realistically reproduce the observed historical climate. Therefore, the analysis starts by evaluating the ability of CORDEX-CORE models to simulate TAS, SR, and WS as they are key to the generation of PVRes and, thus, to the renewable energy sector as a whole. Based on data availability, we used the 1981–2005 reference period that is available for the observational and model datasets. This reference period is widely used in renewable energy studies (Costoya et al. 2021; Ogunjobi et al. 2022). To ease the evaluations, we used Climate Data Operators (CDO) to regrid all datasets to a common resolution of the CRU TS v4.05 reference data.
To compute the reliability metrics, we used the Satellite Application Facility on Climate Monitoring (CM SAF; Kothe 2022). CM SAF is a toolbox that automates the calculation of several model evaluation metrics in R Programming Language (R Core Team 2020). Some of these metrics include correlation (R), root mean square error (RMSE), and mean absolute error (MAE). A summary of the process we followed is given in Fig. 2.
Conceptual framework followed in the present study. CRU, Climate Research Unit; ERA5, European Centre for Medium-Range Weather Forecasts Reanalysis version 5. REMO, Regional Model; GERICS, The Germany Climate Service Center; COSMO, the Consortium for Small-Scale Modeling; RegCM, Regional Climate Model; TAS, ambient temperature; WS, wind speed; SR, downwelling surface shortwave radiation; Tcell, cell temperature; PVRes, solar photovoltaic energy resources, far future: 2025–2055; near future: 2075–2100
where RSDS is the shortwave downward radiation at the surface given in W/m2 and PR is a performance ratio that considers the effect exerted by temperature on the efficiency of PV cells (Jerez et al. 2015). PR can be expressed mathematically as follows:
Here, (gamma) refers to the power thermal coefficient for mono-crystalline silicon cells which indicates how strongly the PV power output is dependent on the temperature of the cells. Here, we use a constant value of − 0.005 °C (Tonui and Tripanagnostopoulos 2008). It is negative because the power output is inversely proportional to increasing cell temperature. TSRC refers to the temperature of the cell under standard test conditions and has a constant value of 25 °C. Tcell refers to a multiple regression model which considers the effects of solar radiation, temperature, and wind speed (Chennai et al. 2007). It can be expressed as shown in Eq. 3:
where Tas is the air temperature around the cells and is given in °C. RSDS is the downward solar radiation at the surface (W/m2), and WS is the near-surface wind speed (m s−1). C1, C2, C3, and C4 are coefficients that are dependent on the properties of the PV materials used. According to Jerez et al. (2015), we apply the following values:
C1 = 4.3 °C,
C3 = 0.028 °C m2 W−1
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