Gul Hameed is a PhD student at the University of Surrey. He holds a BS degree in mechanical engineering from the Pakistan Institute of Engineering and Applied Sciences and an MSc in renewable energy systems engineering from the University of Surrey. Previously, Gul’s research spanned thermal energy storage via phase change materials during his undergraduate studies and catalyst development for the reverse water-gas shift process as a research assistant during his master's program. He also worked on developing energy planning models to achieve net zero targets as a part of his master's research. His research interests cover decarbonizing process systems, the development of algorithms (using machine learning, surrogate modelling, and optimisation) for green processes, low-carbon chemical and fuel production, and the exploration of sustainable energy systems.

My qualifications

Renewable Energy Systems Engineering, MSc
University of Surrey
Mechanical Engineering, BS
Pakistan Institute of Engineering and Applied Sciences (PIEAS)


Research interests


Gul Hameed, Purusothmn Nair S. Bhasker Nair, Raymond R. Tan, Dominic C.Y. Foo, Michael Short (2023)A novel mathematical model to incorporate carbon trading and other emission reduction techniques within energy planning models, In: Sustainable Production and Consumption40(September 2023)pp. 571-589 Elsevier

A dependable and sustainable energy supply is crucial as energy consumption continues to rise due to population growth, economic development, and improved living standards. The use of fossil fuels leads to CO2 emissions and are subject to volatility in prices. Capital-intensive technologies to reduce emissions are challenging to implement on a practical scale, and economic instruments are likely to play a role in future energy systems by encouraging adoption of these technologies. Carbon trading is an emerging economic instrument that enables entities (plants, governments, etc.) to exchange emission rights, allowing economic and environmental aspects to be balanced. This study introduces a scalable carbon trading modelling approach, integrated into previously developed DECO2 open-source energy planning framework. Direct and indirect optimisation approaches are proposed, both consisting of superstructure-based mixed-integer nonlinear programming formulations. Carbon price is a variable in the direct optimisation or a parameter in the indirect optimisation approach. While the direct optimisation approach involves more non-linearity, it is shown to result in solutions with greater decarbonisation, higher profits, and lower costs, compared to the indirect optimisation results. A novel feature of this multi-period model not considered in previous works is the simultaneous emissions trading across time periods and among entities (power plants and government). This enables efficient and coordinated emission allowances trading among various entities and timeframes. Various new costs and revenue streams are added into the energy planning framework; therefore, profits can also be predicted, along with predictions of electricity prices. New energy resources (nuclear and wind) and carbon capture utilisation and storage are also introduced to the modified DECO2 model. The models are tested on the Pakistan’s power sector. Minimisation of emissions using direct optimisation showed that the carbon trading increased profits significantly in the second, third, and fourth planning periods (4.74, 3.86, and 3.55 times, respectively), but in the first period, profits were slightly higher without carbon trading (1.06 times more). Minimisation of budget using indirect optimisation showed higher profits in case of no carbon trading for all the periods. Between 2021 and 2040, hydropower is expected to grow the most (by a minimum of 3.14 times and a maximum of 15.87 times), followed by solar (with an expected increase between 2.54 and 3.26 times) and wind generation (which may increase by 2.35 to 2.66 times). Deployment of emission reduction technologies is significantly lower when carbon trading is implemented as compared to when it is not, due to increased pressure on CO2-intensive generation. Results show that incorporating carbon trading into an energy market leads to both financial (increased profits) and environmental (lower emissions) sustainability, and that using direct optimisation approach increases benefits of carbon markets.

Gul Hameed, Muhammad Ahsan Ghafoor, Muhammad Yousaf, Muhammad Imran, Muhammad Zaman, Ali Elkamel, Azharul Haq, Muhammad Rizwan, Tabbi Wilberforce, Mohammad Ali Abdelkareem, A.G. Olabi (2022)Low temperature phase change materials for thermal energy storage: Current status and computational perspectives, In: Sustainable energy technologies and assessments50101808 Elsevier Ltd

Latent heat based thermal energy storage technology is quite promising due to its reasonable cost and high energy storage capacity. This technology is partially developed. The accelerated transition from non-renewable to renewable energy sources have attracted researchers to shift their focus towards demonstrating thermal energy storage utilizing latent heat at commercial level. Phase change materials utilizing latent heat can store a huge amount of thermal energy within a small temperature range i.e., almost isothermal. In this review of low temperature phase change materials for thermal energy storage, important properties and applications of low temperature phase change materials have been discussed and analyzed. Thermal energy storage technologies are compared in terms of technology readiness levels. Various techniques to improve the heat transfer characteristics of thermal energy storage systems using low temperature phase change materials have also been discussed. Moreover, the use of computational techniques to assess, predict and optimize the performance of the latent energy storage system for different low temperature applications is also presented. In this article, researchers and domestic energy management sector will find comprehensive guidelines for the performance improvement and technology selection for energy management via thermal energy storage.