To mitigate greenhouse gas (GHG) emissions within the constraints of a limited government budget, efficient and effective energy system planning is crucial. While extensive research has been conducted on regional energy systems planning, there is a growing focus on community-scale planning due to its closer connection to end-users. In this study, a community-scale energy system planning model is developed for the City of St. Catharines in Ontario, Canada. This model aims to minimize energy system costs while meeting municipal emission and budget goals and properly allocating conventional and renewable energy sources to the end-users. It addresses the generation, expansion, and emissions costs of five energy sources (i.e., grid electricity, pipeline gas, community solar, community wind, and gas recovery) over a 30-year planning horizon. Uncertainties associated with the community-scale energy system planning process are addressed using the Fuzzy Flexible Programming (FFP) technique through the imposition of tolerance intervals and penalties. Optimized solutions will be generated for both the planning of conventional energy, as well as the expansion of renewable. The results will not only formulate cost-effective allocation and expansion strategies for the city’s energy system but also provide valuable insights for municipal decision-makers, assisting them in more effectively planning and managing energy systems under uncertainties.