1. Dynamic Optimization and Energy Management for Smartgrids , 2013 - 2016 , Funding Agency: Energy Market Authority - National Research Foundation $1,537,000.00
This project leads to design, development, and test bedding of innovative technologies for the next generation of Smart Grids is being carried out. This project addresses two aspects of Smart Grid modelling and simulation. First one relates to the development of intelligent control and optimization algorithms, and the second one relates to their implementation and testing in hardware, both at small microgrid level in the lab, as well as, on moderately large scale level using a Real Time Digital Simulator (RTDS) to simulate a Smart Grid. This project, which is a collaboration between the National University of Singapore and industrial partner SP PowerGrid, aims to develop intelligent computational tools incorporating efficiency optimisation and decision making algorithms that will equip grid operators with enhanced capabilities to receive automated grid fault diagnosis, carry out scenario planning and make optimised decisions in managing the Smart Grid. A total of 10 journal papers and 17 international conference papers have been published/accepted based on the work performed for this project.
2. Uncertainty Quantification Using Type-2 Fuzzy Systems , 2012 - 2014 , Funding Agency: Australian Research Council Discovery Project
This research addresses the gap between advanced fuzzy logic systems and the concept of prediction intervals for dealing with and quantifying uncertainties. The idea is to quickly construct reliable prediction intervals for estimated values using interval type-2 fuzzy logic systems. The approach overcomes the failings of point predictions under uncertainty present in complex systems. Constructed prediction intervals will assist various industries and organisations, which are facing increased pressure to improve the efficiency and effectiveness of their decision-making processes under presence of uncertainties.
3. Computational Tools for Optimal Planning and Scheduling of Distributed Renewable Energy Sources; 2008-2012; Funding agency: NRF; S$787K
This project investigated system security, reliability and planning issues pertaining to embedded distributed generators, and developed advanced optimization techniques and computational tools for reliability analysis for planning and scheduling in a distributed energy system, containing a large amount of solar power generation. The uncertainties arising from the generating capacity and demand were taken into consideration. The proposed optimization tools find the best combination of resources that maximize reliability, security and efficiency, while minimizing emissions and related costs.
4. Modular Distributed Energy Resource Network (MODERN); 2008-2011; Funding agency: A*STAR; S$ 1.887M
The overall objective of this research was to develop environmentally-friendly, resilient, and sustainable energy supply systems to provide energy independence at the least cost. Specifically, it aimed to develop (1). Distributed energy generation and storage systems using diverse energy sources and storage methodologies; (2) Optimal logistics network/s for the uninterrupted and timely supply of a variety of traditional (oil, gas, coal, etc.) and alternate fuels (bio-diesel, methanol, hydrogen, etc.) to the above systems; (3) Interface for plug-n-playmicrogrids of energy generation and storage systems, which can be integrated into an existing power grid as and when required; and (4) Optimal mix and configuration of alternate fuels and storage systems. The methods and tools developed in the project can be used to enable the future energy generation and distribution to be flexible, respond fast to the demands, and facilitate energy trading and energy independence through a judicious mix of generation and storage technologies and their management.
5. Multi-Agent System for Management of Complex Traffic Networks; 2007-2011; Funding agency:Ministry of Education (MOE); S$ 128K
Conventional and traditional traffic signal control methods have shown limited success in optimizing the timings in traffic signal controllers due of the lack of accurate mathematical models of traffic flow at an intersection and uncertainties associated with the traffic data. In order to improve the global view, communication and learning capabilities needs to be incorporated in the computing entity to create a model of the neighbouring computing entities. Several multi-agent systems that provide such an distributed architecture with learning and communication capabilities were developed in this project. Performance of the proposed multi-agent based traffic signal controls for different traffic simulation scenarios were evaluated using a simulated urban road traffic network of Singapore. Comparative analysis performed over the benchmark traffic signal controls – Hierarchical Multi-agent Systems (HMS) and GLIDE (Green Link Determine) indicated considerable improvement in travel time delay and mean speed of vehicles when using proposed multi-agent based traffic signal control methods.
6. Intelligent Mobility Modeling and Management (IMMM); 2010-2012; Funding agency: Ministry of Education (MOE); S$ 170K
This project aims to develop Intelligent Mobility Modeling and Management (IMMM) approaches to facilitate greater and better mobility for smart sustainable transportation systems. The objective of the proposed intelligent mobility management system is to develop new decision making technologies in the field of hybrid intelligent systems which aid the fleet management, vehicle scheduling, intelligent congestion-aware routing and energy management. It will serve to fuse advancements in the fields of computational intelligence, multi-objective optimization, and scheduling and routing for application in an important area of Singapore’s economy. Cutting-edge computational intelligence techniques will be effectively developed and applied to improve urban mobility towards sustainable systems. The project will involve the application of advanced ITS (Intelligent Transportation Systems) technologies using real-time information about road conditions to implement management strategies for mitigating the effects of external events, managing short-term demand for existing capacity, and providing dynamic route guidance to travelers with information about travel conditions, alternative routes, and other modes.