Research Groups

The Electric Power Network Economics Laboratory (EPNEL) in the department of electrical engineering at Seoul National University directed by Professor Jong-Keun Park and Yong-Tae Yoon is well established as a source of expertise in the area of Smart Grid, renewable energy, and the reliability of power system. In 2010, EPNEL was formed in order to enhance collaborative research by merging two laboratories, Power Systems and Economics Lab. founded by Professor Park in 1983 and Power System Economics Lab. established by Professor Yoon in 2004.

The advent of Smart Grid significantly expands opportunities and challenges of entire electric power system industry. EPNEL has researched on the topics that build our energy system clean, stable, and economic through the Smart Grid. Our aim from the start was to provide theoretical solutions to those issues. We, EPNEL, believe our research will enrich the sustainable energy system.

EPNEL conducts research on future power networks by combining power system and electricity market studies with AI- and optimization-based approaches. The laboratory focuses on analyzing real-world challenges in power grids and electricity markets, while developing intelligent decision-making methods using artificial intelligence, optimization, and data-driven techniques.

The Power Systems and Electricity Markets team studies the changing structure of power networks and markets driven by renewable energy, distributed energy resources, and virtual power plants. Key research topics include distribution network reconfiguration, electricity market pricing and penalty mechanisms, VPP bidding strategies, P2P energy trading, blockchain-based REC and energy trading systems, and advanced alarm processing for power system operation. These studies integrate power system analysis, market modeling, mathematical optimization, and data-driven approaches.

The AI and Optimization team develops intelligent algorithms for solving complex operational problems in power systems. The team applies reinforcement learning, machine learning, deep learning, and heuristic optimization to real-time distribution network reconfiguration, automated grid operation, renewable energy variability management, and distributed energy resource scheduling. In particular, EPNEL uses AI not only as a forecasting tool, but also as a core technology for supporting operational and market decisions in modern power systems.

By integrating power engineering, artificial intelligence, optimization, and energy economics, EPNEL aims to provide practical solutions for real-world power systems and electricity markets. Through these efforts, the laboratory contributes to building intelligent energy systems that improve the reliability, flexibility, and economic efficiency of renewable-rich future power networks.

[1] J. Oh, S. Oh, G. Lee, Y. T. Yoon, and S. Cho*, “Sequential Control of Individual Switches for Real-time Distribution Network Reconfiguration Using Deep Reinforcement Learning,” IEEE Transactions on Smart Grid, Jun. 2025.

[2] S. Cho*, J. Oh, J. Lee, S. Oh, H. Moon, C. Zhang, R. Gadh, and Y. T. Yoon, “Hybrid Genetic Algorithm With k-Nearest Neighbors for Radial Distribution Network Reconfiguration,” IEEE Transactions on Smart Grid, Oct. 2023.

[3] H. Kim, Y. Jung, S. Kim, H. Kim, Y. Jin*, and Y. T. Yoon, “Pricing mechanisms for peer-to-peer energy trading: Towards an integrated understanding of energy and network service pricing mechanisms,” Renewable and Sustainable Energy Reviews, Sep. 2023.

[4] Y. Chu, S. Kim, Y. Song, Y. T. Yoon, and Y. Jin*, “Blockchain-based REC System for Improving the Aspects of Procedural Complexity and Cyber Security,” IEEE Access, Feb. 2024.