This contribution is the first from a series of articles dedicated on the use of Earth observation (EO) within the Renewable Energy (RE) sector. While we will continue exploring different sub-sectors of RE, for now we focus exclusively on solar energy.
The drying-up of non-renewable energy resources and the need to cut emissions and minimise as much as possible the negative anthropogenic impact that led to climate change, has caused many debates. As a result, humanity is continuously looking into renewable energy (RE) solutions, as a cleaner and potentially low-cost source, compared to non-renewables. Its use has been strongly promoted on a high level by the United Nations (SDG 7 aiming at “Ensuring access to affordable, reliable, sustainable and modern energy for all”), the European Commission (Renewable energy directive), and many others. The Green deal itself largely promotes and advocates the uptake of renewables.
Many of the up-and-coming trends in the RE sector are related to the exploitation of solar energy through the development and deployment of photovoltaic (PV) systems. This is, to a large extent, due to technological developments (reduction of cost in manufacturing PV systems with high energy efficiency) and a marked paradigm shift in policies in many countries across the world that favour or even subsidise the implementation of PV installations. In turn, this leads into solar becoming the world’s cheapest source of energy by the 2030s, and the one attracting the largest investments. This megatrend is also observed inside the e-shape project, with many pilots focussing on the role Earth observation can play in better exploiting solar energy.
Pilot 3.1 (nextSENSE: solar energy nowcasting & short-term forecasting system) has a two-fold purpose: upstream – for site selection analyses for large-scale solar farms, and downstream – for influence of meteorological factors for the nowcasting and short-term energy yield forecasting. Satellite and numerical weather model based (e.g. from Copernicus CAMS and EUMETSAT/Meteosat) data is used for the purpose and the nowcasting and forecasting deriving from it address notably the need of transmission system operators (TSO) managing the electric grid on different temporal and spatial scales.
Pilot 3.2 (High photovoltaic penetration at urban scale) addresses another big trend in PV: urban PV production. As an increasing amount of people live in cities, around 75% of the global energy supply is consumed in urban environment, and the percentage is expected to grow even further with the progressive adoption of electric vehicles as means of public transport. This increasing need for energy can be addressed adequately by urban PV technologies, as it is more efficient when the energy is consumed where it has been produced, and PV energy production does not affect negatively the air quality of the areas where solar plants are installed. Urban PV systems can be implemented on different scales – surely, there is the individual PV system on a rooftop, the way we are used to think about it, but another possibility is to have a set of larger PV installations on big urban surfaces such as the roofs of supermarkets, parking shades, etc. A challenge in both cases is the shadow effect caused by buildings themselves, roof superstructures and vegetation. To address this, there is an already established and growing market of solar: high-resolution solar cadastre. This means identifying available spaces with strong solar potential and high efficiency for installation of PV in the city, with a corresponding solar resource evaluation for each, taking into account the shadow effect and the local orientations of the roofs. Within e-shape, the next step for PV development at urban scale is being addressed. For example, the variability induced in the urban electric by PV is addressed through providing forecasts on short-term scales, allowing distribution system operators and households to make informed and efficient grid management decisions. These forecasting abilities stimulate the creation of new jobs in energy-trading, and specifically in re-grouping portfolios of PV systems: one can become a part of such a portfolio in order to get information allowing to sell energy at the best price available on the energy market. Another way EO contributes to urban energy planning, is through mapping the different zones of a city, i.e. where people live in the city and where they work, to understand better the amount, regularity, and distribution of electricity needs (e.g. foresee daytime consumption in office areas). Such data is readily available and can contribute to more efficient production and consumption of energy.
Compared to other energy sources, solar has advantages, as well as disadvantages: the amount of surface needed to produce a certain amount of energy is much larger in the case of solar and this can be a potential barrier to social acceptance. Providing the adequate space for PV and solar development has proved to be extremely challenging in some countries, and a solution is to share the surface with other activities (e.g. agriculture, fisheries) and enable its dual use. EO provides an excellent source for that, and interesting combinations of land use have emerged, such as the use of artificial lakes and water bodies to deploy floating PV development.
Another emerging trend is the use of bifacial PV modules: modules that are sensitive on both sides, which can be placed vertically and require less surface, while increasing the surface of PV – with possible consequences on architectural integration of PV into buildings. Moreover, bifacial PV use both the direct sunlight and its reflection off the ground to capture more light. This new factor – the complex way how the ground is reflecting the sun – described by the spectral bidirectional reflection distribution function (BRDF) can as well be mapped as relevant data are operationally obtained by EO satellites.
Another important aspect is the efficiency of PV modules. This depends notably on the energy distribution within the light spectrum, as different PVs have different spectral sensitivities. If one has data for the spectrum of light reaching the PVs, the best PVs can be selected to fit the particular spectral distribution of energy in a specific location or climate. The variability is mostly dependent on presence of aerosols (dust, sea salt, black carbon, etc.), water vapor, ozone and other gases in the atmosphere. This information is available through in situ data and numerical weather modules (e.g. CAMS provides information on water vapor, ozone, and aerosol). With the advancement of PV technology there is a “zoo of PV technologies”, to choose from in order to increase efficiency while decreasing at the same time the costs.
As for policy trends, beyond the abovementioned commitment on behalf of UN and EU, renewable energy policies are very country-specific (with different overall energy needs and needs for different types of energy). However, they often point at the same goal: increase the share of renewables within the country. As a consequence, the implementation means can coincide in different countries, and one example is self-consumption of solar energy; in May 2017, France allowed users having a PV installed to self-consume the energy when possible. Later, a similar law passed in Spain and in other countries, reflecting the trend of self-consumption. Another general policy trend in RE within Europe is the tendency for a country to push the RE sector to develop by itself relying less (if at all) on incentives. And the reason for this independence form government help lies in the maturity of the sector, which has reached a point to offer competitive products and services based on (already) cheap and renewable sources. Given the importance of exploiting renewable energy as a means to achieve the EU Green Deal goals and the SDG 7, and in view of the role EO can play in that regard, the e-shape Market Trends Observatory will continue monitoring relevant trends and generating appropriate insights.
This article is based on contributions from: Philippe Blanc (MINES ParisTech) and Lionel Menard (MINES ParisTech).