Developing optimal demand response aggregation in Singapore's wholesale electricity markets

• Main Author: Lin, Alvin Yao Liang
• Other Authors: Gooi Hoay Beng
• Format: Final Year Project
• Language: English
• Published: 2017
• Subjects: DRNTU::Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/70871
• Institution: Nanyang Technological University

As National Electricity Market of Singapore (NEMS) announced about the new modified market rules in March 2016 and a pilot project built on demand side management implementing on October 2016, there is a need to implement the Demand Response (DR) Program that actively allows consumers to adjust their energy requirements based on the prices from the demand side bidding. Small and medium sized consumers will be able to gain benefits by using DR aggregation as it will compile their potential DR schedules and constraints when taking part in the DR Program. This report will allow readers to have a deeper understanding of the DR program tailored for Singapore’s wholesale electricity market. Firstly, the market clearing model (MCM) used in Singapore is introduced, it is followed by understanding of the constraints used in the mathematical formulation of the developed MCM program. The developed MCM program will then be used to simulate the current MCM to produce an optimal dispatch schedule and the market clearing prices for energy, reserve and regulation. Secondly, studies was conducted on the DR programs available in the market and the framework for the DR program for Singapore was examined to modify the current MCM program with the DR constraint implemented. Thirdly, the current MCM program and MCM with DR program are analysed using numerical analysis to illustrate the function of these programs. Case study simulations are carried out to demonstrate how the DR program can be used effectively and the different factors that can be adjusted to attract more participants into the DR program and allowing the program to perform at its optimality.