The rapid evolution of modern electric power distribution systems into complex networks of interconnected active devices, distributed generation (DG), and storage poses increasing difficulties for system operators. The large-scale integration of distributed energy resources (DERs) and the rapid exchange of measurement data via communication networks present major opportunities for advancing grid operations but also introduce greater uncertainty, higher data dimensionality, more complex network and device models, and challenging control and optimization problems. Deep reinforcement learning (DRL) algorithms are promising in addressing these challenges. However, they have not been effectively adapted for power systems applications, requiring extensive customization for implementation and evaluation. This has resulted in reproducibility challenges and a steep learning curve for researchers new to applying DRL algorithms to the power systems domain. To bridge these gaps, this tutorial aims to serve as a valuable resource for researchers interested in exploring learning-based algorithms to operate active power distribution networks. Specifically, this work presents a generalized process for translating sequential decision-making problems in power distribution systems into Markov decision process (MDP) formulations, illustrated through concrete grid service examples. Additionally, we introduce a simple environment design strategy to develop and evaluate example DRL algorithms for distribution system applications, complete with an included code repository to guide users through environment construction.

The current practices for restoring critical services in the distribution system during a disaster, align with the traditional centralized ideology of distribution systems operations. A central processor evaluates the distribution system after a disruption and attains a restoration plan. However, the centralized operational paradigm is susceptible to single-point failures, requires full situational awareness of the distribution system, and poses scalability challenges for large multifeeder distribution systems. This motivates a distributed decision-making paradigm where multiple agents solve smaller subproblems and jointly coordinate their individual decisions to achieve the global/network-level objective. Toward this goal, we propose a layered architecture for distributed algorithms for resilience and a two-stage distributed algorithm for distribution system restoration. The proposed distributed decision-making framework enables the bottom-up restoration of the distribution system using all available resources, including distributed generation, while only requiring local awareness and limited communications with neighboring connected regions. The proposed framework is robust to single-point failures, enables autonomy using distributed algorithms, and had reduced computational cost compared to centralized optimization solutions.