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Dr Pei-Hao Li
Central House, 14 Upper Woburn Place
Tel: +44 (0)20 3108 7801
  • 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

Research Themes
Research Summary

Current research activities and interests

  1. Discrete choice models to identify influential factors for consumer's behaviours
  2. Demand-side flexibility
  3. Supply-side flexibility
  4. Extension of TIMES modelling abilities
  5. Machine learning in energy

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