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Research Grants
Ongoing
projects (Total funding: S$ 8 Million for the following 5 projects):
1. Risk Assessment And Uncertainty Management Framework For Smart
Grids, 2015-2018, Funding Agency: Ministry of Education (MOE)
This
research project aims to develop intelligent computational algorithms and
tools for an automated, self-healing grid, taking into account the
complexity and vulnerabilities of the stochastic distributed power sources
and uncertain loads. It will provide new capabilities in active grid
management, big data collection with information extraction, and prediction
and modelling under uncertainty, and develop tools for grid operation and
management to incorporate various uncertainties and reduce the risk of
power disruption.
2. Intelligent Information Management System In Smart Buildings
Using Multi-Agent-Enabled Wireless Sensor Actuator Networks, 2014-2017, Funding Agency: NRF/BCA
This research focuses on
system-level amalgamation of state-of-the-art technologies in wireless
communication, network management and computational intelligence. The main
intention is to deploy and manage the wireless sensor-actuator network in
an energy efficient manner using multi-agent design and use the real-time
information from sensors.
3.
Innovative Power System
Control And Energy Management For Solar PV Hybrid Systems Using Forecasting
And Multiobjective Optimization Techniques For
Advanced And Economic Diesel Replacement Business, 2014-2017; Funding Agency: NRF (Energy
Innovation Research Programme)
This research project aims to
develop an innovative power system control and energy management for solar
PV hybrid systems, which generate electricity at the least cost at any
given point-in-time, while ensuring grid stability and lowest loss of load
probability (LOLP) levels. This will be achieved by developing innovative
multi-objective optimisation algorithms, designing and implementing
high-performance power electronic converters and applying leading-edge
forecasting techniques on supply and demand side to minimise both diesel
run-hours and battery size while maintaining grid stability and meeting the
load at any time.
4. Dynamic Optimization
And Energy Management For Smart Grids, 2013-2016, Funding
Agency: NRF (Energy Innovation
Research Programme);
The
future grid is expected to include a large number of distributed renewable
generation sources, energy storage facilities and load control. It is also
expected to be flexible and scalable. A sudden large increase in load
should not impact the stability and reliability of the grid. Such new
expectations, if imposed on today’s grid, would significantly exceed the
capabilities of today’s systems - in terms of their ability to handle
outages, sudden changes in load demand and intermittent generation. Hence,
advanced control and management technologies would be critical to operate
the modern power system. 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 highly accurate scenario planning
and make optimised decisions in managing the smart grid.
5. Power Grid
Stability With an Increasing Share of Renewables (such as Solar) in
Singapore , 2013-2016, Funding Agency: NRF (Competitive Research
Programme)
This
research project aims to address and solve one of the most paramount and
pressing topic the Singapore power grid operator and regulators face in the
next decade: how to ensure a stable and reliable grid operation with an
increasing share of electricity generation coming from widely-distributed,
intermittent renewables, especially solar photovoltaics (PV). The goal of
this research is to analyze the Singapore Power
grid in its entirety with respect to its suitability for a share of up to
30% of its energy supply from intermittent generation, to identify and
address the potential for optimisation in system design, components and
system management, and to develop suitable innovative solutions for the
required operations management, as well as for balancing demand and supply
during times of intermittency (including active load management).
Recently
completed projects:
1. 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.
2. Computational Tools for Optimal Planning and Scheduling of
Distributed Renewable Energy Sources; 2008-2012; Funding agency: NRF;
S$787K
This project investigated
system security, reliability and planning issues pertaining to embedded
distributed generators, and developed advanced optimization techniques and
computational tools for reliability analysis for planning and scheduling in
a distributed energy system, containing a large amount of solar power
generation. The uncertainties arising from the generating capacity and
demand were taken into consideration. The proposed optimization tools find
the best combination of resources that maximize reliability, security and
efficiency, while minimizing emissions and related costs.
3. Modular Distributed Energy Resource Network (MODERN);
2008-2011; Funding agency: A*STAR;
S$ 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-play microgrids 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.
4. 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.
5. 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.
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