PREFERENCE-BASED MATCHING FOR DISTRIBUTED ENERGY RESOURCE ALLOCATION: A CASE STUDY OF QATAR

Abstract

The global transition toward decentralized energy systems needs efficient, sustainable, and economically feasible energy markets. However, Qatar’s current energy market remains highly centralized, with most consumers living in rental houses, limiting their ability to invest in renewable energy systems like rooftop solar panels. This structural issue hinders the efficient integration of Distributed Energy Resources (DERs) and restricts consumer participation in renewable energy adoption. A significant research gap exists in developing structured mechanisms that facilitate direct energy transactions between consumers and DER providers, particularly in regions like Qatar where renewable energy adoption is still in its early stages. This research proposes a novel approach to local energy trading by matching DER providers with consumers based on their preferences, utilizing the Deferred Acceptance Algorithm (DAA) to optimize energy transactions and enhance market efficiency. Real-world energy consumption data from Qatar and a modified IEEE 37-bus network were integrated to simulate energy exchange dynamics. The System Advisor Model (SAM) was employed to generate realistic solar energy production and storage data under Qatar’s climatic conditions, ensuring accurate modeling. Findings indicate that local energy trading significantly reduces grid dependency, particularly during peak solar hours, by facilitating efficient, preference-based matching between consumers and DER providers. The proposed framework enhances economic feasibility and environmental sustainability by allowing consumers to prioritize energy sources based on cost and emissions. Results show that photovoltaic (PV)-only systems yield the shortest payback period (4.4 years), whereas PV with battery energy storage systems (BESS) (7.3 years) and PV with electric vehicle (EV) charging (11.9 years) improve grid flexibility and resilience. The study also underscores the importance of dynamic pricing, where electricity costs adjust based on real-time demand and availability, promoting load balancing and market efficiency. Beyond its technical contributions, this research aligns with Qatar’s National Vision 2030, supporting its renewable energy integration, emissions reduction, and smart grid development objectives. Policy recommendations emphasize financial incentives for DER providers, regulatory frameworks for decentralized energy trading, and infrastructure investments in energy storage and EV charging stations. Future research should focus on implementing pilot LEM projects in Qatar, developing AI-driven trading mechanisms, and enhancing cybersecurity for decentralized transactions. This study presents a scalable and intelligent market design that improves grid efficiency and economic viability while fostering a resilient, decentralized, and low-carbon energy ecosystem in Qatar. Demonstrating the effectiveness of structured, preference-based energy allocation, this research contributes to the advancement of next-generation smart grids and sustainable energy markets worldwide.

Date of Award	2025
Original language	American English
Awarding Institution	HBKU College of Science and Engineering