The purpose of this research is to develop a comprehensive modeling and strategy development tool for small modular reactor (SMR)’s application as combined heat and power cycle (CHP) systems in support of carbon neutrality. The developed model is based on combining thermodynamic system model, CO$_2$ release models, and various economic analysis models. For the simulation of CHP system behavior, first, a flexible and accurate coupled system model was developed by using off-design component models along with modified two-phase effective-NTU method. Then, a CHP heat exchanger system model was developed and coupled for off-design SMR-CHP analysis. Various economic models and new indexes (e.g. break-even prices and break-even carbon pricing) were proposed. CO$_2$ release models were also developed to support comprehensive SMR-CHP feasibility analysis. Economic analysis of SMR-CHP applications was performed both for regulated and reregulated markets. For regulated markets, when the market fossil fuel prices are higher than the derived break-even prices, SMR-CHP heat application was found more beneficial than SMR’s full power operation. In general, replacing fossil fuels with SMR-CHP heat application is found more beneficial when the market fossil fuel prices are high. However, for deregulated markets with varying electricity retail price rates, carbon pricing may sometimes be required when rates are high. In this case, the developed system tool can predict the required carbon prices for the SMR-CHP heat application to both replace fossil fuels and overcome the opportunity cost of foregoing large profit from selling electricity at high prices. Nonetheless, the limit of carbon pricing may not need to go above $70/tCO$_2$ for SMR-CHP heat application to be economically feasible. To perform multi-objective optimization analysis, non-dominated sorting genetic algorithm-II (NSGA2), a type of multi-objective evolutionary algorithms (MOEA) was used in conjunction with Latin hypercube sampling method using three different objectives (levelized cost of electricity, saved amount of CO$_2$ releases, and annual profit) to maintain the statistical representation of the overall distribution of the input parameters without clustering. By using the MOEA with similar total number of simulations for different objectives, several different configurations for the SMR-CHP system parameters were found to provide better results in all three objectives compared with the case of using the classical weighted-sum method. To demonstrate the practical utility of the methodology, techno-economic analysis was performed for two representative regions with district heating system in the Republic of Korea: One region (Sejong) has higher residential energy demand and another (Cheongju) has higher industrial energy demand. For region-specific analysis, 2030 energy demands (for district heating and electricity) were predicted using the machine learning regression tree method. An optimal design configuration was chosen from set of solutions given by the previous MOEA analysis and coupled with predicted regional CHP heat demand profiles. The results of this dissertation suggest that 1) the proposed SMR-CHP application can be economically feasible in many cases with little to no carbon pricing, 2) MOEAs can give optimal results with acceptable computational costs even compared with classical methods, 3) SMR-CHP can replace significant amount of fossil fuels for both power generation and district heating applications for Sejong with high residential demands, and 4) SMR-CHP system may not be able to replace significant part of the Cheongju’s industrial demands. Nonetheless, using SMR-CHP allows stronger economical cases to than just using full power generation mode, and it can also help prevent large amount of CO$_2$ releases by increasing its efficiency and meeting regional heat demands.