# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 1 Introduction  
The shift towards digitalizing distribution power grids marks a pivotal step in modernizing energy infrastructure, emphasizing decentralization and renewable energy adoption. As distribution grids encounter increased variability and the integration of distributed energy resources (DERs), the necessity for dynamic grid management escalates. Digitalization serves as a transformative force, revolutionizing grid planning, operations, and market interactions. The current infrastructure, supported by technologies like supervisory control and data acquisition (SCADA) systems and intelligent electronic devices (IEDs) in medium voltage (MV) networks, provides a solid base for adopting more advanced digital solutions by distribution system operators (DSOs). However, the challenge lies in handling the vast, complex data generated by smart grid (SGs), which stretches the limits of traditional data processing methods. Artificial intelligence (AI) offers, for instance, breakthroughs in load forecasting, grid stability, fault detection, and security, as well as enhancing smart grid reliability andresilience. The potential of AI underscores the importance of further exploration and implementation in developing fully optimized SG systems [1]. This transformation is underscored by multiple studies [2; 3; 4; 5] and is driven by advancements in the Internet of Things (IoT), artificial intelligence (AI), edge computing, advanced metering infrastructure (AMI), and blockchain. Moreover, the development of electric digital twins and service-oriented architectures marks a shift towards more dynamic, automated, and resilient energy systems [2; 3]. This indicates a future where energy systems are adaptable to changing demands and environmental challenges [2; 4; 5]. The successful digital transformation of the power sector extends beyond technology to strategic coordination [6]. As demonstrated by companies like Jibei Electric Power Company, this transformation needs to align digital efforts with broader organizational goals, enhancing power transmission, distribution capabilities, and internal digitalization [6]. Recent scholarly works offer insights into the complex nature of SG technologies. They illustrate the impact of digitalization and renewable energy integration on power systems and consumers [5; 7; 8; 9; 10; 11; 12]. From decentralizing renewable energy generation to leveraging broadband over power line infrastructure for unified data exchange, these technologies balance supply and demand and enhance grid intelligence. This systematic literature review explores the expansive terrain of digitalization in power distribution grids, categorizing our analysis into three primary domains: grid planning, grid operations, and the energy market. Utilizing a robust search methodology, we identified a substantial corpus of literature from IEEE Xplore, encompassing both conference papers and journal articles, totaling 13,700 articles in the planning domain, 31,859 in operations, and 9098 in the energy market from 2014 to January 2024. To ensure transparency and reproducibility, this section details the procedure followed in our study using Python version 3.10.13 scripting to manage and analyze data related to digital technologies in power distribution grids. The process begins with the initialization and importing of essential libraries, including os for file operations, pandas for data manipulation, NumPy for numerical operations, and matplotlib.pyplot and seaborn for data visualization. Next, the directory containing the CSV files is defined, with the scholarly articles obtained from an advanced IEEE Xplore search and listing all CSV files in this directory to process them systematically. Then, we initialize empty lists to store dataframes categorized by their relevance to planning, operations, and market. Each CSV file is read and, based on the category inferred from the file name, the data are appended to the respective list (planning, operation, or market).