A Review of Scalable and Privacy-Preserving Multi-Agent Frameworks for Distributed Energy Resources

TL;DR

This paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems.

Abstract

Distributed energy resources (DERs) are gaining prominence due to their advantages in improving energy efficiency, reducing carbon emissions, and enhancing grid resilience. Despite the increasing deployment, the potential of DERs has yet to be fully explored and exploited. A fundamental question restrains the management of numerous DERs in large-scale power systems, "How should DER data be securely processed and DER operations be efficiently optimized?" To address this question, this paper considers two critical issues, namely privacy for processing DER data and scalability in optimizing DER operations, then surveys existing and emerging solutions from a multi-agent framework perspective. In the context of scalability, this paper reviews state-of-the-art research that relies on parallel control, optimization, and learning within distributed and/or decentralized information exchange structures, while in the context of privacy, it identifies privacy preservation measures that can be synthesized into the aforementioned scalable structures. Despite research advances in these areas, challenges remain because these highly interdisciplinary studies blend a wide variety of scalable computing architectures and privacy preservation techniques from different fields, making them difficult to adapt in practice. To mitigate this issue, this paper provides a holistic review of trending strategies that orchestrate privacy and scalability for large-scale power system operations from a multi-agent perspective, particularly for DER control problems. Furthermore, this review extrapolates new approaches for future scalable, privacy-aware, and cybersecure pathways to unlock the full potential of DERs through controlling, optimizing, and learning generic multi-agent-based cyber-physical systems.

Figures (7)

• Figure 1: Review structure of scalable and privacy-preserving multi-agent frameworks for DERs.
• Figure 2: Three typical information exchange structures of networked multi-agent systems for managing DERs in power systems: (a) Centralized information exchange that relies on a system operator to collect information from all agents, process it, and then send control commands to each agent; (b) Distributed structure that allows agents to operate independently, interact with coordinator/environment, and communicate with each other over a network; and (c) Decentralized structures that is similar to a Distributed structure, but without agent-to-agent communications.
• Figure 3: Conceptualization of a privacy-preserving DER management system.
• Figure 6: Illustration of privacy breaches from external eavesdroppers, honest-but-curious agents, and the coordinator in a three-agent distributed information exchange structure: The External eavesdroppers wiretap all communication channels in the network; Agent three is an Honest-but-curious agent who attempts to infer other agents' private information based on its accessible information; The Coordinator might have access to agents' private data and/or critical system information.
• Figure 9: Secure communications between a sender and a receiver using a cryptosystem.

Theorems & Definitions (3)

• Remark 1
• Remark 2
• Remark 3