Optimizing photovoltaic systems to decarbonize residential arctic buildings considering real consumption data and temporal mismatch

Nunavik is a remote region in northern Quebec, Canada relying on off-grid diesel-based electricity production. In this study, photovoltaic (PV) systems for residential buildings are optimized using real electricity consumption data. The region is characterized by a significant temporal mismatch between electricity demand and PV production. Two PV systems are studied: standalone arrays and building-integrated systems (BIPV). Three multiobjective optimization problems are formulated to represent different ways to manage the demand-production mismatch, involving objectives such as the mean squared error between production and consumption, penetration of solar energy, energy gap between production and usage, and PV size. Solutions were obtained using a genetic algorithm (NSGA-II). It was found that moderately sized PV systems (60–140 m2) could cover about one third of the instantaneous electricity demand of a semi-detached house, yielding an average annual GHG emissions reduction of 1.112 ton CO2 per house. An important surplus was found with two optimization problems, suggesting a potential for reinjection in the microgrid or batteries. Optimal designs of PV systems for both configurations were influenced by how the mismatch is managed, i.e. the choice of objective functions. The first optimization problem minimized excess energy by favoring less sunny directions, while the second and third supported energy storage or surplus reinjection, favoring south facing or vertical PVs. A robustness analysis underscored the importance of matching PV system design to consumption profiles. Ultimately, this study contributes to the emerging field of renewable energy integration in the Arctic, aiming to reduce reliance on fossil fuels.