PLAIN Nicolas

No summary available.

Jointly achieving the United Nations Sustainable Development Goal 7 of access to clean and reliable energy for all by 2030 and the climate goals of the Paris Agreement requires the development of microgrids (MGs), powered by local renewable energy resources, for remote areas that cannot be connected to the grid. This is particularly the case in sub-Saharan Africa where 600 million people, mainly in remote rural areas, do not have access to electricity. This thesis focuses on the analysis of off-grid solar MGs (MGSIs) to address the challenges of power generation in remote areas of the African continent. We first explore the multi-scale temporal variability of the solar resource in Africa and its implication on the sizing of MGSIs, using high-resolution satellite data of global horizontal irradiance for a period of 21 years (1995-2015). Taking into account periods of low resources leads to oversizing the photovoltaic (PV) area by a factor of 1.3 to 4. With such an oversizing, it is possible to ensure a good quality of service without depending on a large storage volume. For some areas, demand flexibility during low resource periods would significantly reduce the sizing.We then analyze how the potential seasonality of the electric demand affects the MGSI sizing, through the analysis of the co-variability structure between the solar resource and the demand. We consider that the MG must meet a total daily demand of at least 95% of the days and a seasonal variation in demand of up to 30%. While in some parts of Africa, the size required to meet seasonal demand is 20% smaller than what is needed to meet non-seasonal demand, it can also be 20% larger. We also explore the extent to which the effect of PV panel tilt angle could reduce the supply-demand mismatch and sizing. Generally, the tilt angle is equal to the latitude. For constant daily demand, the size gain obtained by optimizing the tilt angle is less than 4%, but for specific seasonal demand patterns, it can reach 9%.Finally, the cost of electricity required to provide good quality of service is a key determinant of potential MGSI deployment. We evaluate the sensitivity of the discounted cost of electricity (LCOE) and the optimal MG configuration (i.e., with the lowest LCOE) to the costs of PV panels, batteries and other economic parameters. While the sensitivity of LCOE to discounted costs is obviously important, the optimal configuration (PV panel area and storage capacity) is very robust. The optimal configuration is almost solely determined by the time co-variability structure between the resource and the demand. It is therefore dependent on the one hand on the regional climate, and on the other hand on the temporal structure of the demand. The adjustment variable is essentially the oversizing of the PV panels, which is based on the low days of solar resource, while the storage has the main function of managing the mismatch between demand and resource at the intraday level. An interesting result is that the LCOE is lower for productive uses of electricity compared to domestic uses only because of the lower storage capacity required for productive uses.

Jointly achieving the United Nations Sustainable Development Goal 7 of access to clean and reliable energy for all by 2030 and the climate goals of the Paris Agreement requires the development of microgrids (MGs), powered by local renewable energy resources, for remote areas that cannot be connected to the grid. This is particularly the case in sub-Saharan Africa where 600 million people, mainly in remote rural areas, do not have access to electricity. This thesis focuses on the analysis of off-grid solar MGs (MGSIs) to address the challenges of power generation in remote areas of the African continent. We first explore the multi-scale temporal variability of the solar resource in Africa and its implication on the sizing of MGSIs, using high-resolution satellite data of global horizontal irradiance for a period of 21 years (1995-2015). Taking into account periods of low resources leads to oversizing the photovoltaic (PV) area by a factor of 1.3 to 4. With such an oversizing, it is possible to ensure a good quality of service without depending on a large storage volume. For some areas, demand flexibility during low resource periods would significantly reduce the sizing.We then analyze how the potential seasonality of the electric demand affects the MGSI sizing, through the analysis of the co-variability structure between the solar resource and the demand. We consider that the MG must meet a total daily demand of at least 95% of the days and a seasonal variation in demand of up to 30%. While in some parts of Africa, the size required to meet seasonal demand is 20% smaller than what is needed to meet non-seasonal demand, it can also be 20% larger. We also explore the extent to which the effect of PV panel tilt angle could reduce the supply-demand mismatch and sizing. Generally, the tilt angle is equal to the latitude. For constant daily demand, the size gain obtained by optimizing the tilt angle is less than 4%, but for specific seasonal demand patterns, it can reach 9%.Finally, the cost of electricity required to provide good quality of service is a key determinant of potential MGSI deployment. We evaluate the sensitivity of the discounted cost of electricity (LCOE) and the optimal MG configuration (i.e., with the lowest LCOE) to the costs of PV panels, batteries and other economic parameters. While the sensitivity of LCOE to discounted costs is obviously important, the optimal configuration (PV panel area and storage capacity) is very robust. The optimal configuration is almost solely determined by the time co-variability structure between the resource and the demand. It is therefore dependent on the one hand on the regional climate, and on the other hand on the temporal structure of the demand. The adjustment variable is essentially the oversizing of the PV panels, which is based on the low days of solar resource, while the storage has the main function of managing the mismatch between demand and resource at the intraday level. An interesting result is that the LCOE is lower for productive uses of electricity compared to domestic uses only because of the lower storage capacity required for productive uses.