• Energy Research

Measurements of daily means of surface solar irradiance made at four ground stations in French Guiana are compared to estimates from the HelioClim-3 database produced by the Heliosat-2 method applied to Meteosat satellite images. The bias ranges from 12 W m−2 6% of the mean of measurements to 23 W m−2 12%, depending on the stations. The root mean square difference ranges between 23 W m−2 11% and 35 W m−2 18%. The correlation coefficient r is close to 0.9. Better results are observed during the rainy season than during the dry season. Uncertainties are mainly due to the presence of clouds, large viewing angles of the Meteosat satellite, and limitations of the atmospheric transmittance model under the tropical atmospheric conditions. It is concluded that the Heliosat-2 method provides new knowledge about solar radiation in French Guiana.

The purpose of this article is to evaluate the potential for solar energy in the Guiana Shield and propose indicators to encourage the exploitation of solar energy systems in this area. For this, we use the Heliosat-2 optimized method to process images from the geostationary meteorological satellite GOES acquired in the period from April 2010 to July 2015. We calculated the average daily global horizontal irradiation (GHI) and direct normal irradiation (DNI) throughout the study period. The results obtained allowed us to establish four indicators: maps of production potential, the inter-day variability of DNI and GHI, maps of solar panel orientation related to maximum solar potential, and maps of areas where the solar resource is under the exploitable potential threshold. We also added an additional indicator, the suitability of areas for solar system installation depending on the ground slope. Our study shows that the average value of production potential for the entire Guiana Shield is approximately 1780 kWh.kWc-1 .year-1 for GHI and 2040 kWh.kWc-1 .year-1 for DNI. Comparisons with pyranometer measurements indicate an error relative bias of less than 2% and a relative RMSE of less than 21% for hourly estimates of GHI. Although the Guiana Shield region is covered by many clouds, few areas show insufficient solar potential for the exploitation of GHI and DNI, but the hilly nature of the area limits possible locations of very large power plants and instead favors more medium-sized plants. This is the first study that offers exploitability indicators for solar resources in the Guiana Shield. In conclusion, the established indicators provide a new perspective on the solar potential in the Guiana Shield and are expected to promote the development of new solar energy operating systems.

Abstract Today, the overall goal of energy transition planning is to seek an optimal strategy for increasing the share of renewable sources in existing power networks, such that the growing power demand is satisfied at manageable short/long term investment. In this paper we address the problem of PV penetration in electricity networks, by considering both (1) the spatial issue of site selection and size, and (2) the temporal aspect of hourly load and demand satisfaction, in addition with the investment and maintenance costs to guarantee a viable and reliable solution. We propose to address this spatio-temporal optimization problem through an integrated GIS and robust optimization model, that allows handling of the ubiquitous dependencies between resource and demand time variability and the selection of optimal sites of renewable power generation. Our approach contributes to the integration of the multi-dimensional and combinatorial aspects of this problem, gathering geographical layers (regional or national scale) and temporal packing (hourly time stamp) constraints, and cost functions. This model computes the optimal geographical location and size of PV facilities allowing energy planning targets to be met at minimal cost in a reliable manner. In this paper, we illustrate our approach by studying the penetration of large-scale solar PV in the French Guiana’s power system. Among the results, we show for instance that: (1) our approach performs geographical aggregation with real contextual data, i.e. balances the intermittency of RE sources by spreading out the corresponding installations (location + size) across the territory; (2) the total installed PV capacity can be doubled by removing the 35% penetration limit on intermittent power without exceeding hourly demand; (3) the safest investment scenario is below 30 MW of new PV facilities ( ≈ 45 M€ and 2 plants), though it is theoretically possible to install up to 45 MW (>120 M€ and 11 plants).

This paper deals with artificial neural network (ANN) applied to control a standalone microgrid in French Guiana. ANN is an artificial intelligence technique used to control non-linear and complex systems. ANN associated with the Levenberg–Marquardt (LM) algorithm has many advantages, such as rapid decision-making and improved system transients. Therefore, this technique should be adapted for the control of photovoltaic (PV) systems in the tropical climate of French Guiana with high variation in irradiance. The microgrid is composed of a PV source and a storage battery to supply an isolated building which is modeled by a DC load. The PV source is controlled by an ANN-based MPPT (Maximum Power Point Tracking) controller. To validate our ANN-MPPT, we compared it with one of the very popular MPPT algorithms, which is the P&O-MPPT algorithm. The comparison results show that our ANN-MPPT works well because it can find the maximum power point quickly. In the case of battery control, we tested two feed-forward backpropagation neural network (FFBNN) configurations called method1 and method2 associated with the Levenberg–Marquardt (LM) algorithm. We varied the number of hidden layers in each of these two FFBNN configurations to obtain the optimal number of hidden layers for each configuration which optimizes battery control. Method1 is chosen because it is better than method2, in a sense that it respects the maximum amplitude of the battery current for our application and improves the transient regimes of this current. This best configuration (method1) is then tested with two other learning algorithms for comparison: Bayesian regularization (BR) and scaled conjugate gradient (SCG) methods. The system performance with LM algorithm is better than SCG and BR algorithms. LM algorithm improves the performance of the system in transient regimes while the results obtained with the SGG and BR algorithms are similar. Then, we focused on the advantage of using ANN control compared to the conventional proportional integral control (PI control). The comparison results showed that ANN control associated with the LM algorithm (ANN-LM) made it possible to reduce battery current peaks by 26% in transient regimes compared to conventional PI control. Finally, we present and discuss the results of our simulation obtained with the MATLAB Simulink software.

Although the high amount of solar irradiance in the tropics is an advantage for a profitable PV production, the local meteorological conditions induce a very high variability which is problematic for a safe and gainful injection into the power grid. This issue is even more critical in non-interconnected territories where network stability is an absolute necessity and the injection of PV power has to be limited. The basis for precise cloud evolution and subsequent irradiance forecasts are high quality atmospheric analyses for NWP. Geostationary meteorological satellites provide valuable observations of cloud properties with high spatio-temporal resolutions and allow a pertinent data assimilation. The shortcoming is that optical and thermal channels of satellite sensors do not provide cloud properties from inside clouds. Different existing data assimilation approaches aim at deriving atmospheric analyses with most realistic cloud features, utilising geostationary satellite observations. The potential of assimilating satellite-derived cloud information in regional NWP with focus on irradiance forecasts in tropical regions has not been evaluated so far. Hence, the present work aims at evaluating the potential of geostationary satellite data assimilation in limited-area models applied to the French tropical oversea territories Reunion Island and French Guiana. 32nd European Photovoltaic Solar Energy Conference and Exhibition; 2318-2321

Increasing the share of renewable energy sources in power systems is key to a successful energy transition. Optimal renewable site selection requires a holistic approach, involving land, resources, environmental and economical data and constraints. In this paper we consider the problem of solar PV penetration into the power network as a spatiotemporal analysis combined with decision support targeted for policy makers and investors. Our goal is to seek new models that maximize energy penetration and stability into the network, while minimizing the operational costs. We show how the selection of solar PV sites can be accomplished to satisfy such objectives by investigating the optimal clustering of multiple solar PV parks around a shared electrical substation. This is a combinatorial problem in terms of all the potential clusters given the set of PV site candidates. Our main contribution lies in identifying and proposing a modeling analogy of our problem with the so-called SONET problem, tackled in fiber network designs. We show how this new spatiotemporal PV park placement model minimizes operational costs, while increasing energy stability of the solutions produced. We also introduce a GIS preprocessing step to reduce the computational cost of the proposed approach. We compare our proposed SONET-based model to an existing GIS-optimization model on a real case study and data from the French Guiana's power system. This new approach aggregates multiple PV parks into clusters distributed across the territory. In the case of French Guiana, the same global nominal power (~ 45MW) can, for instance, be distributed among 11 PV parks and 3 clusters, against 3 large-scale PV parks. Results show substantial gain in costs per kWh produced, up to 10MW of extra installed power and 16GWh of extra power generation when considering PV parks 5MW. The new cluster configuration also ensures improved energy stability of the solutions, resulting in mitigation of the risks for both the network manager and the decision maker.