Work

A Multi-Objective Optimization Framework for Peer-to-Peer Energy Trading in South Korea’s Tiered Pricing System

Year: 2024

Type: article

Abstract: This study proposes a multi-objective optimization framework for peer-to-peer (P2P) energy trading in South Korea’s tiered electricity pricing system. The framework employs the Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) to optimize three conflicting objectives: minimizing consumer costs, maximizing prosumer benefits, and enhancing energy utilization. Using real microgrid data from a South Korean community, the framework’s performance is validated through simulations. The results highlight that MOEA/D achieved an optimal cost of KRW 32,205.0, a benefit of KRW 32,205.0, and an energy utilization rate of 57.46%, outperforming the widely used NSGA-II algorithm. Pareto front analysis demonstrates MOEA/D’s ability to generate diverse and balanced solutions, making it well suited for regulated energy markets. These findings underline the framework’s potential to improve energy efficiency, lower costs, and foster sustainable energy trading practices. This research offers valuable insights for advancing decentralized energy systems in South Korea and similar environments.

Source: Applied Sciences

DOI: 10.3390/app142311071

Authors: Laura Kharatovi, Rahma Gantassi, Zaki Masood, Yonghoon Choi

Institutions: Chonnam National University, Singapore University of Technology and Design

Language: English

Topic: Smart Grid Energy Management

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: gold

Estimated APC paid: $2,728