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

Optimal integrated energy systems design incorporating variable renewable energy sources

The effect of variability in renewable input sources on the optimal design and reliability of an integrated energy system designed for off-grid mining operation is investigated via a two-stage approach. Firstly, possible energy system designs are generated by solving a deterministic non-linear programming (NLP) optimization problem to minimize the capital cost for a number of input scenarios. Two measures of reliability, the loss of power supply probability (LPSP) and energy index of reliability (EIR), are then evaluated for each design based on the minimization of the external energy required to satisfy load demands under a variety of input conditions. Two case studies of mining operations located in regions with different degrees of variability are presented. The results show that the degree of variability has an impact on the design configuration, cost and performance, and highlights the limitations associated with deterministic decision making for high variability systems

On the design of complex energy systems: Accounting for renewables variability in systems sizing

The variability challenge inherent in the design and sizing of stand-alone renewables-based energy systems incorporating storage is addressed at the design stage. The framework developed for reliability evaluation combines the stochastic modelling of renewable resources with chronological simulation of energy system performance for the evaluation of system reliability. The effect of inter-year variability is quantified by using a modified form of the loss of power supply probability as the reliability objective. A bi-criteria problem of capital cost minimization and reliability maximization is solved for two cases of remotely-located mining operations in Chile and Canada to demonstrate the capabilities of the methodology. Approximations to the Pareto-optimal fronts generated using a multi-objective genetic algorithm (NSGA-II). The performances of the minimum-cost designs generated are investigated in each case. The methodology provides the decision maker with necessary information about a number of alternative designs based on which sizing decisions may be made

Optimal design of hybrid energy systems incorporating stochastic renewable resources fluctuations

The potential impacts of variability and intermittency in renewable resources on the design of stand-alone renewables-based energy systems incorporating storage are addressed at the design stage. The framework developed accounts for climate-based variability by considering different stochastically generated renewable input scenarios in the evaluation of system reliability. Operational constraints which control the availability and discharge of storage technologies based on previous storage states and technology start-up times are incorporated into the energy system model to account for the intermittent power output from renewables. A cost-reliability bi-criteria sizing problem is solved for two cases of a remote Canadian mine to demonstrate that intermittency in generation can influence technology choices, system configuration and system operation. Approximations to the non-dominated fronts are generated with NSGA-II, and the operating characteristics of the maximum-reliability designs generated in the cases are investigated. The methodology provides the decision maker with information about a number of operable designs and an understanding of the performance risks associated with the selection of any given design