Investment Planning Models and Optimal Incentive Design for System Planners and Investors to Integrate Renewables
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Emissions from fossil fuel based energy sources is a global concern with respect to environmental degradation. Thus, a diversification of energy sources in the supply mix of power systems to include renewable sources of energy has become necessary in order to reduce emissions. In addition to renewable integration, the incorporation of energy conservation also helps in emissions reduction, thereby, becoming an increasingly important aspect of generation expansion planning (GEP). However, the relatively high cost of renewable energy sources (RES) is a hindrance in achieving a cleaner and more diverse energy supply mix. Therefore, it is imperative to develop and analyze a system planning model for determining optimal incentives that will encourage both renewable integration and conservation, while allowing investors to make optimal investment decisions on RES projects. In recent years, solar energy, particularly solar photovoltaic (PV), based generation has become one of the fastest growing energy sources in the electricity sector. Hence, the first part of this thesis presents a novel sensitivity analysis framework, based on duality theory (DT), to examine the sensitivity of an investor's profit to changes in parameters of a solar PV investment planning model previously proposed. The computed sensitivity indices are utilized for assessing the risk of a specific solar PV investment project for a realistic model of the Ontario grid. The results demonstrate that sensitivity indices obtained using DT-based method are very close to the true sensitivities obtained using a finite difference (FD) approach and also those obtained using Monte Carlo simulations, but at lower computational costs. Furthermore, a novel interpretation of the sensitivity indices is developed, by proposing mathematical formulas that help to evaluate the risk indices of a solar PV investment project. In the second part of this thesis, a novel holistic GEP model, from a system planner's perspective, has been proposed to enable a central planning authority (CPA) or a regulator to determine optimal incentives for renewable energy integration and energy conservation, while considering investors' constraints. The proposed GEP model is also designed to determine the siting, sizing, timing, and technology of the new capacities required to adequately supply the demand over the planning horizon. Various case studies relevant to Ontario and based on realistic data, comprising presence/absence and variations in RES penetration and/or energy conservation targets, variations in maximum payback-period limits of RES, and other input parameter changes are presented and discussed. Furthermore, Monte Carlo simulations are performed to understand the effects that uncertainties on non-dispatchable wind and solar generation availabilities have on the GEP outcome, particularly on the optimal RES incentives.