Intelligent computational infrastructures for optimized autonomous distributed energy generation in remote communities

Distributed generation along with smart grid applications are poised to make important contributions to the clean-tech sector and remote communities. The dependence on one source for energy supply does not prove reliable enough when the renewable resource, such as wind or solar, is variable, creating a dependence on external fuel supply and a vulnerability to foreign control. Developing an energy strategy through intelligent energy system simulation and optimization can help communities make informed decisions about their energy investments. This dissertation reasons that distributed renewable energy systems without operative computational infrastructures face a fundamental economic challenge derived from their ad-hoc design and implementation. To address this, it proposes the method of Optimal Operational Awareness (OOA)—a feedback mechanism on the state of, and changes in, the properties of the implemented subsystems and their behaviour, to meet users objectives. Despite many applications of hybrid renewable energy systems, and reputable multi-objective evolutionary algorithms (MOEAs) for optimization, no one has applied MOEAs to dynamic system operation for optimized engagement of system components. This thesis describes an application of the NSGA-II algorithm to the multi-objective optimization of the operation of a stand-alone wind-PV-biomass-diesel system with batteries and CAES storage and a central controller. The simultaneous objectives are to minimize the levelized cost of energy (LCOE), and unmet load (UL) while maximizing the renewable energy ratio (RER). This work provides a case-study evaluation from data collected on-site at the island of Fernando de Noronha (FDN), Brazil. The results show that FDN could move from an annual average of 33% RER and LCOE range of $0.26 - $0.36 per kWh to an increased RER range of 60% - 100% and LCOE of $0.10 - $0.50 per kWh, while maintaining UL of 0%, by increasing its renewable energy generation and storage capacity ap