PRIVACY-PRESERVING TRADING IN LOCAL ENERGY MARKETS
Authors: Erdayandi Kamil 
Published: October 2024

The transition towards sustainable energy, primarily driven by the urgent need to combat climate change and reduce environmental degradation, has led to a significant shift from conventional fossil fuels to Renewable Energy Sources (RES). Local Energy Markets (LEMs) have emerged as innovative platforms enabling RES integration by facilitating energy trading among participants. By offering trading incentives, LEMs encourage adopting renewable energy, hence promoting a greener and more sustainable energy landscape. Despite their potential, LEMs face critical challenges that hinder their broader adoption. Notably, extensive data sharing is essential for their operation and introduces significant privacy risks to users. Addressing this challenge requires LEMs to deploy advanced privacy-enhancing technologies, which brings new challenges -- high computational intensity and lack of transaction accountability. In addition, by doing so, economic incentives for users should be preserved. To address these challenges, this thesis proposes novel solutions for privacy-preserving trading in LEMs. The main contributions of the thesis are briefly summarised below.The first contribution of the thesis involves a detailed examination and discussion of the privacy-preserving trading mechanisms in LEMs. We start with a broad overview of LEMs by investigating trading mechanisms and privacy-preserving methods, establishing a solid foundation for the subsequent thesis designs. Through a comparative study of existing market models focused on privacy preservation, we ensure a complete review of the current state of the art. Furthermore, we delve into a thorough discussion of the potential frameworks for LEMs. This effort aims to analyse LEM frameworks from a technical perspective, thereby forming a base for further developing solutions for privacy-preserving LEMs.Building on this foundation, as a second contribution, the thesis presents a novel decentralised, Privacy-Friendly Energy Trading Platform (PFET), which adopts a game-theoretical framework, particularly leveraging the Stackelberg competition model. PFET stands out from current models by creating a competitive marketplace where market dynamics such as prices and demands are calculated from the competition. To protect sensitive information, including sellers' prices and buyers' demand levels, the platform utilises Homomorphic Encryption (HE). This enables buyers to compute the total demand placed on sellers in an encrypted manner, protecting participant privacy throughout the process. Our performance evaluations affirm PFET's effectiveness in maintaining user privacy within the context of a competitive market environment.The third contribution presented is a Privacy-Preserving Clearance Mechanism for Local Energy Markets (PP-LEM) designed to enhance trading efficiency within a semi-decentralised environment. PP-LEM adopts a competitive approach based on game theory, specifically utilising the Stackelberg Game, emphasising computational efficiency and privacy. Through the application of HE, PP-LEM ensures the protection of sensitive information for all parties involved, facilitating secure calculations on encrypted data without disclosing actual information. Our extensive evaluation showcases the capability of PP-LEM to deliver a clearance mechanism that is not only privacy-preserving but also computationally efficient, outperforming existing approaches. It distinguishes itself by providing computational efficiency and protecting user welfare without trade-offs. This contribution significantly contributes to the domain of privacy-preserving LEMs, offering a novel solution that provides incentive mechanisms and privacy protection with computational efficiency. Lastly, the thesis presents the Privacy-Preserving and Accountable Billing (PA-Bill) protocol tailored for peer-to-peer energy trading markets. PA-Bill tackles the issue of mismatches between committed and actual energy deliveries, ensuring both privacy and accountability. It features a novel universal cost-splitting mechanism that leverages HE for privacy protection and blockchain technology to maintain accountability. Moreover, it incorporates a dispute resolution mechanism designed to reduce billing inaccuracies while upholding accountability. Our research confirms that PA-Bill effectively offers a billing solution that preserves privacy within a semi-decentralised market setup, ensuring an efficient and secure trading environment.Overall, this thesis contributes to the advancement of LEMs by proposing a comprehensive framework that addresses privacy concerns, enhances computational efficiency, maintains economic incentives for clearance mechanisms, and provides accountability for billing mechanisms. Through its novel contributions, this research paves the way for successfully integrating renewable energy sources into the grid, benefiting both RES owners and the broader community, promoting a sustainable and resilient energy future.