PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems

Abstract: We present the PowerGridworld software package to provide users with a lightweight, modular, and customizable framework for creating power-systems-focused, multi-agent Gym environments that readily integrate with existing training frameworks for reinforcement learning (RL). Although many frameworks exist for training multi-agent RL (MARL) policies, none can rapidly prototype and develop the environments themselves, especially in the context of heterogeneous (composite, multi-device) power systems where power flow solutions are required to define grid-level variables and costs. PowerGridworld is an open-source software package that helps to fill this gap. To highlight PowerGridworld's key features, we present two case studies and demonstrate learning MARL policies using both OpenAI's multi-agent deep deterministic policy gradient (MADDPG) and RLLib's proximal policy optimization (PPO) algorithms. In both cases, at least some subset of agents incorporates elements of the power flow solution at each time step as part of their reward (negative cost) structures.

• The paper introduces PowerGridworld, a modular open-source framework that integrates with leading RL platforms to advance multi-agent reinforcement learning applications in power systems.
• The framework enables rapid prototyping and customization for complex, heterogeneous power grids by incorporating power flow solutions into state observations and reward structures.
• Case studies in building coordination and heterogeneous multi-agent scenarios demonstrate its potential for scalable, decentralized energy management in real-world environments.

Overview of "PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systems"

The paper under examination introduces PowerGridworld, an innovative, open-source framework designed to enhance multi-agent reinforcement learning (MARL) applications specifically within power systems. This software suite provides a modular and customizable environment built to integrate seamlessly with established reinforcement learning (RL) training platforms, notably OpenAI's multi-agent deep deterministic policy gradient (MADDPG) and RLLib's proximal policy optimization (PPO) algorithms.

The paper's motivation roots itself in the increasing complexity of modern power systems, requiring decentralized approaches due to the nonlinear dynamical models and heterogeneous objectives of different devices. Classical control methods struggle to provide efficient solutions under such conditions, necessitating robust data-driven techniques like MARL.

Key Features of PowerGridworld

PowerGridworld distinguishes itself by offering several capabilities absent in traditional MARL frameworks:

1. Rapid Prototyping Capability: Unlike other frameworks, PowerGridworld enables rapid prototyping and creation of training environments within composite and heterogeneous power systems where standard power flow solutions dictate grid-level variables and costs.
2. Modular Environment Construction: It allows users to construct and customize power-systems-focused multi-agent environments using a lightweight and modular approach.
3. Integration with RL Frameworks: The framework is designed to integrate with popular RL libraries, thus enabling efficient policy training utilizing existing algorithm implementations.
4. Power Flow Integration: The software includes mechanisms for incorporating power flow solutions into the state observation and reward structures of the agents.

Comparative Analysis

The document provides a comparative analysis within the broader scope of similar MARL environments, notably contrasting with PettingZoo, CityLearn, and GridLearn. Each framework serves specific niches and limitations, with PowerGridworld providing the flexibility of user-defined step sizes and unlimited agent customization which competitors lack.

Case Studies and Applications

The paper elucidates PowerGridworld’s application through two compelling case studies:

• Multi-Agent Building Coordination: This scenario involves homogeneously modeled agents managing resources within buildings. It demonstrates system coordination to achieve thermal comfort and uphold voltage support, thereby aligning local actions with broader grid objectives.
• Heterogeneous Multi-Agent Scenario: Here, agents embody smart building management, PV array control, and EV charging station operations, each with distinct goals. The study emphasizes the versatility in modeling diverse systems with PowerGridworld, showcasing successful MARL policy training using RLLib's PPO.

Theoretical and Practical Implications

The implications of PowerGridworld are both theoretical and practical. Theoretically, it enables exploration into new algorithms and methods for MARL applications in power grids. Practically, its integration with HPC resources through RLLib represents a significant step forward in efficiently scaling reinforcement learning tasks in power systems as their complexity rises.

Future Directions

Looking forward, PowerGridworld could facilitate expanded research in MARL-based decentralized control systems in energy management and broader adoption of RL-based optimizations in complex system architectures. The identified limitations such as synchronous, fixed-frequency stepping and simple communication models indicate areas for future development, including the potential for more complex communication protocols and time-stepping strategies.

In conclusion, PowerGridworld not only establishes itself as a valuable tool for researchers but also represents a significant advancement in bridging existing theoretical frameworks with practical real-world applications in power systems.