Isolated electrical microgrids employing renewable energy resources:analysis of the electrification of remote communities in Peru.

This project points out a brief overview of several concepts, as Renewable Energy Resources, Distributed Energy Resources, Distributed Generation, and describes the general architecture of an electrical microgrid, isolated or connected to the Medium Voltage Network. Moreover, the project focuses on a project carried out by GRECDH Department in collaboration with CITCEA Department, both belonging to Universitat Politécnica de Catalunya: it concerns isolated microgrids employing renewable energy resources in two communities in northern Peru. Several solutions found using optimization software regarding different generation systems (wind and photovoltaic) and different energy demand scenarios are commented and analyzed from an electrical point of view. Furthermore, there are some proposals to improve microgrid performances, in particular to increase voltage values for each load connected to the microgrid. The extra costs required by the proposed solutions are calculated and their effect on the total microgrid cost are taken into account; finally there are some considerations about the impact the project has on population and on people's daily life.

Agent-based simulation for renewable energy incentive design

In this thesis, we propose a novel approach to model the diffusion of residential PV systems. For this purpose, we use an agent-based model where agents are the families living in the area of interest. The case study is the Emilia-Romagna Regional Energy plan, which aims to increase the produc- tion of electricity from renewable energy. So, we study the microdata from the Survey on Household Income and Wealth (SHIW) provided by Bank of Italy in order to obtain the characteristics of families living in Emilia-Romagna. These data have allowed us to artificial generate families and reproduce the socio-economic aspects of the region. The families generated by means of a software are placed on the virtual world by associating them with the buildings. These buildings are acquired by analysing the vector data of regional buildings made available by the region. Each year, the model determines the level of diffusion by simulating the installed capacity. The adoption behaviour is influenced by social interactions, household’s economic situation, the environmental benefits arising from the adoption and the payback period of the investment.