Artificial intelligence driven real-time decision-making framework for hierarchical energy management system (Hi-EMS) under dynamically changing scenarios

• Main Author: Tan, Jeremy Min Ze
• Other Authors: Gooi Hoay Beng
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering::Electric power
• Online Access: https://hdl.handle.net/10356/167629
• Institution: Nanyang Technological University

Large power volatility is faced by electric power grid due to the increasing number of renewable energy (RE) sources such as wind and solar energy penetrating into the conventional grid. To solve this problem, artificial intelligence (AI) is used for real-time decision making. AI is also increasingly popular in the field of RE research as it can provide fast real-time response. In this context, the use of deep reinforcement learning (DRL) can provide continuous control to the power factor of the solar photovoltaics (PVs) , reactive power of the capacitor banks (CBs) and tap settings of the on-load tap changer (OLTC) transformer to adapt to the rapidly changing voltage in the power system. This helps in reducing the power losses and voltage fluctuations in the system.