The dual challenges of increasing extreme weather events and the urgent need for low-carbon energy systems demand resilient and adaptive energy infrastructure planning. Achieving ambitious renewable energy and emissions reduction goals necessitates integrated approaches to address both gradual climate shifts and sudden weather extremes. This dissertation develops an integrated modeling framework to analyze renewable energy resources, power systems, and air quality dynamics. The framework combines tools such as geographical information systems (GIS), optimal power flow (OPF), numerical weather simulation (WRF), and chemical transport modeling (CMAQ), all harmonized within a unified weather regime for holistic system insights. Developed entirely in Python and publicly accessible via GitHub, the framework has been validated using meteorological and air quality observational datasets. The model replicates daily fluctuations in weather and pollution comparably to established benchmarks. Projections for future scenarios suggest significant reductions in winter PM2.5 and summer ozone levels but highlight that growing electricity demand from building and transportation sector electrification could offset emissions reductions achieved through thermal power phase-outs.Complementary to the modeling framework, this dissertation explores novel electricity market designs to integrate emerging resources and support the clean energy transition. New resources, such as battery energy storage, distributed energy resources, and grid-enhancing technologies, differ substantially from traditional fossil fuel generators and require market adaptations. First, a generalized locational marginal price (LMP)-based energy market model is proposed for internal controllable HVDC lines (ICLs) under a single system operator. This model allows ICL operators to bid competitively for bidirectional power flow, optimizing cost savings and congestion relief. Second, as fast but energy-limited resources like electric storage gain prominence, their energy endurance alongside response speed is assessed. Improved energy accounting and price formation reveal limitations in conventional reserve constructs, with recommendations for enhanced reliability and efficiency. Finally, a stochastic optimal power flow model evaluates reserve provisions from demand-side flexible resources, incorporating uncertainties in renewable generation and ambient temperature forecasts via chance constraints. Results demonstrate significant reserve potential on the demand side, contingent on customer participation in demand response programs. By bridging integrated energy modeling with innovative market designs, this research provides actionable insights for advancing a resilient and sustainable clean energy transition.