Fuzzy Optimization Model of an Incremental Capacity Auction Formulation with GHG Consideration

An incremental capacity auction (ICA) is a mechanism to procure future generation capacity in a power system. Greenhouse gas (GHG) emissions from generators negatively affect our climate and there is a real need to reduce them. Thus, it is critically important for ICA models to procure future generation capacity that reduces GHG emissions. In this thesis, we propose two ICA models incorporating energy-limited generation (renewables and storage) and a GHG emission constraint. All offers are converted into unforced capacity, negating any effect of energy limitations of generation offers. The first ICA model uses classical optimization and considers GHG emission limits and maximizes social welfare (SW). The second ICA model uses a fuzzy optimization technique to simultaneously optimize the objectives of SW maximization and GHG emission minimization. Both ICA models are tested on two datasets, a 10 and 338 capacity supply offer case which are constructed using Ontario data. While both models control GHG emissions as desired, the ICA model with fuzzy optimization is shown to find a better balance between maximizing net SW and minimizing GHG emissions. This is seen by the superior reductions in GHG emissions ranging from 33.36% up to 484.8% at the expense of minor decreases in SW ranging from 0.745% to 6.98%. Results demonstrate how GHG emission reduction results in increased selection of low carbon generation.