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- Research Associate
- Bartlett School Env, Energy & Resources
- Faculty of the Built Environment
Dr Li is an experienced TIMES modeller
and energy model developer. Since he joined UCL Energy Institute, he has worked
closely with BEIS to maintain UK TIMES (UKTM) model and provided consultancy
services to BEIS on modelling, which contributed to key policies, including 5th
Carbon Budget and Clean Growth Strategy. Besides the consultancy task, Dr Li devotes to developing innovative modelling approaches in the UKTM framework to
better represent supply-side flexibility, demand-side flexibility and consumer
behaviours/preferences to provide insights for decarbonisation transitions.
Before joining UCL Energy Institute,
Dr Li worked at Industrial Technology Research Institute, Taiwan to facilitate
Taiwanese government to make energy policies on low-carbon energy developments,
such as INDC and long-term energy plan, with Taiwan TIMES model. He developed
several national scale energy models including Taiwan TIMES model and Taiwan
2050 Calculator (cooperated with DECC) to provide evidence-based insights for
low-carbon development in Taiwan. He also developed low-carbon development
plans for two islands in Taiwan based on evidence of GHG inventory and optimisation
planning models. Those plans had then been approved by the Taiwanese
government.
In addition
to the aforementioned experiences, Dr Li has also worked on a wide range of research
areas, including water network design, water resource management, climate-based
water resource predictions, air pollution monitoring, waste management, health
impact analysis and decision support system.
Expertise and Skills: energy system modelling, water resource
management, air pollution modelling, optimisation, machine learning, heuristic
algorithm, geographic information system, computer programming, database
management


Current research activities and interests
- Discrete choice models to identify influential factors for consumer's behaviours
- Demand-side flexibility
- Supply-side flexibility
- Extension of TIMES modelling abilities
- Machine learning in energy