Multi-objective optimization applied to energy scenarios and power system design

The European Green Deal objective is to transform the EU into the first climate-neutral continent by 2050. However, designing power systems heavily reliant on Variable Renewable Energy Sources (VRES) introduces complex challenges and inherently conflicting objectives. To tackle these problems, this thesis develops and tests a multi-objective optimization algorithm by integrating Differential Evolution (DE) with advanced multi-objective techniques, and applies it to the bottom-up dispatch model COMESE. Originally designed as a single-objective tool focused on minimizing total system costs by optimizing technology installation and hourly electricity production, the algorithm has been updated to solve a Constrained Multi-Objective Optimization Problem (CMOP). The study focuses on the evaluation of four performance metrics: the Levelized Cost of Timely Electricity (LCOTE), greenhouse gas emissions, mineral depletion, and land occupation. Environmental coefficients were calculated using a Life Cycle Assessment (LCA) approach, strictly separating structural (embedded) from operational (direct) impacts. The algorithm was then tested by simulating a progressive integration of nuclear fission as baseload generation within an Italian power system projected to 2050, initially entirely reliant on renewable energy sources. The simulations demonstrated that the integration of a nuclear baseload significantly alters the system architecture, shifting it from a more expensive and impactful network, heavily reliant on intermittent renewables and massive electrochemical storage, to a more efficient and sustainable configuration. By generating three-dimensional Pareto fronts, this work quantifies the costs of prioritizing a single metric over others. The study concludes that there is no single ideal configuration for the energy transition, but rather a spectrum of optimal compromises, providing decision-makers with an essential tool for balanced energy system planning.