The objective of this research is to develop advanced techno-economic and sustainability analysis tools to support the adoption of emerging grid technologies. The electric power industry is undergoing profound transformations on its way toward net-zero emissions. While numerous novel grid technologies have been introduced, their adoption is often hindered by a lack of demonstrated cost-effectiveness. Therefore, stakeholders need advanced tools to assess the value of emerging grid technologies and make the best deployment plans. Evaluating grid technologies presents a dual challenge. First, conventional techno-economic assessment (TEA) typically focuses on the device level. However, emerging grid technologies often enable new grid functionalities, requiring a system-level evaluation. In this context, simulation results, such as those provided by power flow analyses, are essential. Second, power systems in different applications feature diverse configurations. Applying a uniform TEA process to all situations is not feasible. Therefore, we propose a modular and expandable TEA tool that integrates simulation capabilities, allowing users to customize the modules based on their specific needs. To achieve sustainability goals and minimize costs during infrastructure upgrades, it's crucial to determine the optimal deployment time of emerging technologies. This need drives the development of an asset management tool that integrates sustainability considerations. We address this problem with two frameworks: one employing a two-stage method based on constraint programming techniques, and the other featuring a constrained multi-agent Markov decision process (CMMDP). The algorithms for solving these proposed models are also within the scope of this research. In conclusion, this research proposes a generic TEA tool and sustainable asset management frameworks to evaluate emerging grid technologies, identify their potential applications, and guide future investment.