Summary:
Renewable energy sources continue to gain acceptance. Nonetheless, two major restrictions prevent further approval of RES: availability of power and cost of equipment. Distributed generation, (DG) grid-tied photovoltaic-wind hybrid systems with centralized battery back-up, are capable of alleviating the variability of renewable energy resources. The drawback, though, is the cost of equipment, necessary to create such a system. Hence, optimization of power generation and storage in terms of capital cost, O&M cost and variability is requisite for the financial feasibility of DC microgrid systems. PV and wind power generation depend on time and are variable, but at the same time, both are highly correlated, which makes them ideal for a binary hybrid system. This thesis presents an optimization technique based on a Multi-Objective Genetic Algorithm (MOGA). The proposed approach employs a techno-economic method to determine the system design, optimized by considering multiple objectives, among which are size, cost, and availability. The outcome is a benchmark system cost, requisite to meet load requirements and which can also be used to monetize ancillary services that the smart DC microgrid can provide the utility with, at the point of common coupling (PCC), e.g. voltage regulation, black starting, etc.
The conclusions drawn from this thesis are that the more we seek to increase the power availability of the interconnected microgrid, the higher the cost of building, operating and maintaining the grid, as the number of RES generators, batteries and the amount of energy drawn from the public distribution network increase in order for the grid to meet the local energy requirements.
Keywords:
Battery, hybrid microgrid, microgrid, multiobjective optimization, NSGA-II, photovoltaic generator, smart grid, wind turbine.
