Reducing the variability of renewable energy systems : evaluation of the robustness of typical energy systems

Our current energy system is shifting from fuel-powered and centralised power generation to small-scaled renewable architectures. However, this energetic crossroad is facing many uncertainties as renewable energy sources involves many intermittency and lack of knowledge due to their recent emergence. This master thesis performs robust design optimization analysis for various energetic scenarios including renewable technologies. The task consists in enhancing the economical robustness of an energy system made of photovoltaic arrays and battery storage. Economical robustness in an energetic context is the propensity to predict accurately the cost of an energetic system, with parameters that could variate (solar irradiance, cost of technologies, demand fluctuations). In other words, the optimal profile of the investigated system should both minimize the cost of it and its stochastic variation caused by uncertain parameters. Robust design optimization of the basic PV-Battery system will be implemented by adding three technologies (micro gas turbine, internal combustion engine, heat pump unit) likely to enhance the economical prediction accuracy of the whole system while satisfying both heat and electrical demands. The duality of demand leads to minimize the Levelized Cost of eXergy for all couplings. Moreover, for each subsystem, uncertainty quantification has been applied to determine how the uncertain parameters are stochastically represented.For all the pairings and various scenarios, the impact of uncertainties on the stochastic output have been quantified. Moreover, the optimized system configurations to lower the mean cost or to provide robustness are known.