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Legacy Digital Transformation is modernizing or migrating systems from non-digital or older digital technology to newer digital technologies. Digitalization is essential for information reading, processing, transforming, and storing. Social media, Cloud, and analytics are the major technologies in today's digital world. Digitalization (business process) and Digital Transformation (the effect) are the core elements of newer global policies and processes. Recent COVID pandemic situation, Organizations are willing to digitalize their environment without losing business. Digital technologies help to improve their capabilities to transform processes that intern promote new business models. Applications cannot remain static and should modernize to meet the evolving business and technology needs. Business needs time to market, Agility, and reduce technical debt. Technology needs consist of APIs, better Security, Portability, Scalability, Cloud support, Deployment, Automation, and Integration. This paper elaborates different transformation/modernization approaches for Legacy systems written in very long or End of Life (EOL) systems to newer digital technologies to serve the business needs. EOL impacts application production, supportability, compliance, and security. Organizations spend money and resources on Digital Transformation for considering Investment versus Return on Investment, Agility of the System, and improved business processes. Migration and Modernization are critical for any Legacy Digital Transformation. Management takes decisions to proceed with Digital Transformation for considering Total Cost Ownership (TCO) and Return on Investment (ROI) of the program. The paper also includes a TCO-ROI calculator for Transformation from Legacy / Monolithic to new architectures like Microservices.

Power flow methods are one of the powerful tools used in the analysis of stable and reliable operation of the electric power systems. Conventional power flow methods make use of slack bus, PV and PQ bus, low R/X ratio in the formulation of the power flow. These assumptions while considering autonomous microgrids (MGs), with small sources and low voltage connection lines, are not suitable. Present MGs incorporate AC and DC sources and loads along with storage and power electronic conversion devices. In light of these facts, a distributed power flow method (DPFM) for autonomous microgrids is presented here that solves the power flow problem node-wise, minimizing losses and does not consider slack, PV or PQ buses. In order to have proper control over the load sharing among the sources, a modified droop control is used. The proposed DPFM can be used for AC low voltage (LV) autonomous microgrid systems with added advantages of remedying the dependency on voltage level and R/X ratio in the formulation itself. DPFM is applied on a 10 bus, low voltage, microgrid system giving a better voltage profile..

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