Design and optimization of hybrid renewable energy systems for off-grid continuous operations

The mining industry accounts for a significant portion of the energy demand by the industrial sector. The rising demand for metals around the world, coupled with the depletion of readily accessible ore deposits, has led to mining operations moving to more remote locations with no grid supply of energy. As a result, the operations require transport of fuel over large distances, leading to a significant increase in the overall mining cost. Renewable energy is considered to be the most promising solution to the mining industry energy problem. This work investigates the possibility of operating remote mines on local generation from renewables. A survey of recent literature revealed that while a lot of research had been done on hybrid renewable energy systems design and sizing, little thought had been given to accounting for the stochastic nature of renewable resources in the sizing process. Previous works focused on the sizing of PV-wind-battery systems; other potential generation and storage technologies were largely ignored. The challenge of intermittency in the power output of renewable generation systems had also largely been ignored. This thesis extends the state of the art on hybrid systems sizing by developing models and methodologies to address these challenges. A novel hybrid energy system integrating thermal and electrical renewable generation options with multiple large scale energy storage options is considered in this thesis. Models are developed for the different components of the energy system, with dynamic models incorporated for the material and energy balances of the storage alternatives, leading to a system of nonlinear differential algebraic equations (DAEs). The temporal nature of the renewable resources is accounted for by considering multiple stochastic renewable input scenarios generated from probability distribution functions (PDFs) as inputs into the system model. A reliability measure to quantify the impact of weather-based variability, called the modified loss of power supply probability, is developed. A bi-criteria sizing methodology which allows for the stochastic nature of renewable resources to be accounted for is presented. The approach combines the time series approach to reliability evaluation with a stochastic simulation model. Two approaches for mitigating the impact of intermittency in power outputs of renewable generation technologies are also developed. The first approach is based on system redesign, while the second approach is based on the introduction of an instantaneous response storage option. Case studies were presented to demonstrate the various methodologies. The results show that climate-based variability can have a significant impact on the cost and performance of hybrid energy systems and should always be accounted for in the sizing process. Intermittency needs to be accounted for in some form at the design stage as it can have an impact on the choice of technologies. The integration of thermal and electrical power generation and storage options provide a way to reduce hybrid system costs. The methodologies developed in this thesis are applicable to any location and can easily be extended to incorporate other generation and storage alternatives. They provide the decision maker with necessary information for making preliminary sizing decisions.