A dual-interval fractional energy systems optimization for Nova Scotia, Canada

In real-world energy systems, uncertainties are diverse and complex. Simply turning multi-objective energy planning into a single objective often does not work. This approach relies on subjective or unrealistic assumptions and also overlooks the trade-offs among economic cost, energy efficiency, and environmental performance. In this study, we developed a dual-interval fractional energy systems programming (DFEP) method to optimize energy systems under multiple objectives and uncertainties, particularly in the form of interval- and dual-interval-fractional representations. The DFEP framework integrated mixed-integer linear programming with fractional and dual-interval programming. This allowed it to handle uncertain parameters expressed as dual-interval boundaries and trade-offs between costs and benefits. This method was applied to Nova Scotia's energy system in Canada. The goal was to support energy planning under greenhouse gas (GHG) emission policies. Eight scenarios were analyzed to evaluate electricity generation, primary energy consumption, facilities expansion, and imported electricity. The results suggested that renewable energy and imported electricity can effectively replace fossil fuels, leading to a significant reduction in GHG emissions. A higher share of clean electricity generation and subsequent decline in fossil fuel use demonstrated the feasibility of achieving zero GHG emissions in electricity generation by 2030. Additionally, current coal-fired and oil-fired plant capacities were sufficient to meet future energy demand. These findings provide valuable insights and robust support for decision-makers in developing sustainable energy policies and achieving GHG mitigation targets.