Privacy-Preserving Local Energy Trading Considering Network Fees

2026-02-27

AI Summary

This work addresses the common oversight in existing local energy market mechanisms—namely, the lack of integrated consideration for privacy preservation, physical grid constraints, and network fee incentives. To bridge this gap, the paper proposes a novel local energy trading protocol that unifies network fee-based incentives with strong privacy guarantees. Built upon an enhanced double auction mechanism, the protocol leverages secure multi-party computation to protect transaction data privacy and incorporates the Schnorr identity protocol to enable verifiable multi-party authentication. This approach represents the first unified framework that simultaneously achieves network fee incentives and high-assurance privacy protection, while significantly reducing communication overhead and interaction rounds. Experimental results demonstrate that, in a representative scenario with 5,000 participants, the protocol completes market clearing within 4.17 minutes, showcasing its efficiency, security, and practical applicability.

Technology Category

• Machine Learning: Privacy
• Multiagent Systems: Mechanism Design
• Search and Optimization: Local Search

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Privacy-enhancing technologies
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Driven by the widespread deployment of distributed energy resources, local energy markets (LEMs) have emerged as a promising approach for enabling direct trades among prosumers and consumers to balance intermittent generation and demand locally. However, LEMs involve processing sensitive participant data, which, if not protected, poses privacy risks. At the same time, since electricity is exchanged over the physical power network, market mechanisms should consider physical constraints and network-related costs. Existing work typically addresses these issues separately, either by incorporating grid-related aspects or by providing privacy protection. To address this gap, we propose a privacy-preserving protocol for LEMs, with consideration of network fees that can incite participants to respect physical limits. The protocol is based on a double-auction mechanism adapted from prior work to enable more efficient application of our privacy-preserving approach. To protect participants'data, we use secure multiparty computation. In addition, Schnorr's identification protocol is employed with multiparty verification to ensure authenticated participation without compromising privacy. We further optimise the protocol to reduce communication and round complexity. We prove that the protocol meets its security requirements and show through experimentation its feasibility at a typical LEM scale: a market with 5,000 participants can be cleared in 4.17 minutes.

Problem

• privacy-preserving
• local energy markets
• network fees
• distributed energy resources
• secure multiparty computation

Innovation

• privacy-preserving
• local energy market
• secure multiparty computation
• network fees
• double auction