The addition of renewable energy in a previously diesel-powered off-grid micro-grid results in what is known in the field as a Stand-Alone Hybrid Renewable Energy System (HRES). Such initiative is a near-term target of both federal and provincial governments in Canada. Not only does it reduce environmental hazards like leaks, spills and air pollution, but the combination of renewable energy and fossil fuel generators can increase stability and lower the cost of electricity. It is deemed a crucial step towards a clean energy future, but also a necessity in the reconciliation process with Indigenous Peoples of Canada - many of who inhabit off-grid communities. The addition of renewable energy can greatly increase the independence of a community by reducing reliability on external diesel suppliers and creating job opportunities.

To be successful, HRES need to be carefully planned;​ the variable and uncertain behaviour of natural resources add a level of complexity to the preliminary design stage. Energy systems are therefore simulated and optimized to estimate the lifecycle cost by determining the nature and capacity of their components and their operational strategy. Chapter 2 goes over the preliminary design stage of two HRES in British Columbian communities. Many modelling tools are available, ranging from full-factorial and linear optimization techniques that can solve single-objective problems, to meta-heuristic algorithms. One of the distinctions between different HRES modelling tools is the foresight horizon being used. Linear programming tools commonly have a perfect foresight over the typical year analysed, for both demand and natural resources. This can lead to an overly optimistic prediction of the lifecycle cost of a system when the reality of implementations comes with uncertainties. On the other hand, tools that use myopic foresight, or no knowledge of future parameters, can lead to pessimistic lifecycle cost estimates since the demand and power output of certain renewable energy technologies, like solar panels, can be known within a few hours. The purpose of Chapter 3 of this thesis is to guide readers towards the right tool in the context of energy system modelling for the preliminary design of HRES. It was found that the degree of importance of choosing the appropriate foresight approach is a function of renewable energy penetration, autocorrelation, and storage capacity. A system with a high renewable energy share, a low short-term (few hours) autocorrelation, and an optimal storage size will result in the highest NPC difference between the two methods. When planning for long-term HRES design, the choice of the foresight horizon can either be representative of a lower/upper cost boundary (perfect and myopic foresight respectively) or of the real-time predictability of the power output of the chosen renewable energy power source.

The use of energy system modelling tools is often reserved for highly qualified personnel and is therefore costly for prospective communities. To improve community readiness with minimal investment, a simple alternative to energy system modelling is proposed in Chapter 4 for the integration of tidal stream turbines in British Columbia. A series of three logical conditions was demonstrated to inform on the viability of a project in terms of cost reduction in comparison to the business as usual scenario. These conditions were found to also be useful for determining the minimum scale, or the economic break-in scale, for a tidal stream turbine given a remote community. In this context, communities are found to be best described by the local price of diesel fuel as an easily accessible metric to represent the current cost of electricity, their electrical load scale, and the local tidal current resource. Ten British Columbian communities were selected to validate the results by comparing the set of conditions to a complete energy system modelling approach and four were found to reach savings of 10 % or more as compared to the business as usual scenario.

The long-term objective of this work is to provide remote communities with an integrated, affordable, and turnkey solution for the displacement of diesel in their energy systems. The next steps in achieving this include augmented optimization tools to quantify and capture non-monetary value so that the modelling and multi-criteria decision-making steps of the design process can be bridged together.