Abstract
Climate change and global warming effects are already changing our planet and can cause irreversible and catastrophic consequences on our lives if the temperature increase will not be kept under the threshold limits established by the scientific community. In such a context, renewable energy technologies became fundamental to increase the share of renewable energies into the energy mix up to the 75% of the total electrical energy production and to reach the aforementioned temperature target. However, the exploitation of renewable resources still presents curtailments that have to be overcome to be able to fully exploit their potential. For example, wind and sun are characterized by a fluctuating and intermittent nature that cannot be controlled and complicate the coordination between the demand and production phases. They introduce stability issues into national power grids when a high share of renewable energy is produced and there is not an equivalent energy demand, wasting thus significant percentages of the total production. Moreover, renewable energy systems still present economic limitations related to high investment costs, which slows down their complete development. In this scenario, Hybrid Renewable Energy Systems (HRES) can contribute to solve the aforementioned problems. In particular, they can be a feasible solution for the electrification of remote places and rural areas that are not reached by the national power grid. But also to help in the formation of independent energy districts with net zero emissions. They can be designed to exploit the specific renewable resources of a site and they can be either coupled with storage systems to match the energy demand and production, or with conventional generators to limit their emissions still providing reliable and sustainable electrification alternatives. This thesis work focuses on two crucial aspects related to the development of HRESs that raise during the design and management phases, namely the optimal sizing of the energy system and the unit commitment of the controllable generators. During both the design and management phase it is fundamental to find the best configuration of the variables involved in the optimization problem and the most convenient modelling of the power generation systems. For what concerns the design phase of a HRES, this manuscript analyzes the optimal sizing of its components that optimize a target function, which can be represented, for example, by the total cost of the system or by the renewable energy production among the others. Often, simplifications about the V VI Abstract assumption of some boundary conditions such as the load profile or the renewable energy production profiles lead to results that do not correspond to the real system behavior and can be misleading in the design phase of a HRES. The first part of this work deals with this aspect. A novel Mixed Integer Linear Programming (MILP) optimization algorithm has been developed to design a tool capable of assessing the optimal sizing of a HRES. The algorithm has been applied to a real case study of a mountain hut located in South-Tyrol (Italy) with a hybrid system composed by solar, wind and diesel generators together with a battery storage. The algorithm compares several scenarios providing the optimal configurations of the HRES, which are characterized by different costs and energy deficits. This tool has been developed to help engineers and system designers to identify the best trade-off between costs and energy deficits during the planning phase of a HRES, still granting the demand of the users as well as the constraints. On the other hand, when dealing with the optimal management of an energy system, the solution for the unit commitment of controllable generators depends on the modelling of some parameters and performance figures that are often over-simplified or neglected, such as the efficiency or the emission curves of Internal Combustion Engines (ICGs) or renewable generators as a function of the load. In the second part of the thesis work, a MILP optimization algorithm to compute the optimal management of an energy system composed by ICGs and different storage systems has been designed to investigate the effects of the aforementioned parameters on the optimal solution. The algorithm has been applied to three different case studies to evaluate different optimization targets constituted by multi objective functions. The developed optimization tool proved to be very flexible and capable to be adopted also in complex systems embedding energy storage devices and renewable energy systems. Results showed the strong sensitivity of the results to the accurate modelling of the devices embedded in the system, thus suggesting a detailed analysis of the problem to get reliable and realistic results.